A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, whi...

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Bibliographic Details
Published in:	Water (Basel) Vol. 11; no. 5; p. 910
Main Authors:	Tyralis, Hristos, Papacharalampous, Georgia, Langousis, Andreas
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.05.2019
Subjects:	Algorithms Artificial intelligence Big Data Classification computer software Decision trees Feature selection Hydrology Machine learning Science Scientists Software Survival analysis Variables water resources water utilities
ISSN:	2073-4441, 2073-4441
Online Access:	Get full text
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Abstract	Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered.
AbstractList	Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered.
Author	Langousis, Andreas Tyralis, Hristos Papacharalampous, Georgia
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Cites_doi	10.1016/j.csda.2006.12.030 10.1016/j.jhydrol.2018.10.044 10.1002/hyp.11298 10.1016/j.jhydrol.2018.02.009 10.1007/s11269-014-0872-z 10.1007/s10533-015-0163-7 10.1016/j.jhydrol.2012.01.039 10.1007/s11269-014-0796-7 10.3390/w10081049 10.1109/ITA.2012.6181810 10.1029/2018JG004613 10.5194/hess-20-4605-2016 10.1007/s11069-018-3246-7 10.1002/2017JG003856 10.1023/A:1010933404324 10.1002/2016JG003503 10.1016/j.isprsjprs.2016.01.011 10.1029/2018WR022643 10.1002/2016WR019034 10.1002/rra.1534 10.1002/2014JD022507 10.1016/j.envsoft.2011.06.004 10.1002/jgrc.20102 10.3390/w7073531 10.1080/02626667.2018.1425802 10.1016/j.patcog.2013.05.018 10.3389/fmars.2017.00141 10.3389/fmars.2018.00317 10.1007/s00477-018-1519-z 10.1016/j.jhydrol.2018.04.038 10.2134/jeq2016.07.0261 10.1016/j.jhydrol.2018.06.081 10.1080/1573062X.2018.1424211 10.1007/s11634-016-0270-x 10.1002/2013WR014203 10.1007/s00477-018-1638-6 10.5194/hess-16-3699-2012 10.1002/hyp.7110 10.5194/hess-19-2409-2015 10.1080/01431161.2018.1433343 10.1038/s41598-017-03011-5 10.1214/ss/1009213290 10.1080/10618600.2019.1677243 10.1016/j.jhydrol.2016.02.017 10.1016/j.advwatres.2017.11.010 10.1016/j.jhydrol.2016.04.049 10.1214/09-AOAS285 10.1016/j.jhydrol.2014.05.029 10.1007/s11222-012-9349-1 10.1007/s00477-018-1523-3 10.3390/w10101372 10.1002/rra.3019 10.5194/hess-20-2611-2016 10.1002/sam.10103 10.3389/fmars.2017.00184 10.1007/s11069-015-1908-2 10.2134/jeq2016.03.0076 10.1016/j.jhydrol.2017.11.026 10.2134/jeq2017.07.0300 10.1002/bimj.201700243 10.1080/10485252.2012.677843 10.1016/j.jhydrol.2004.06.021 10.1016/j.jhydrol.2015.12.012 10.1016/j.jhydrol.2014.03.057 10.1890/11-0252.1 10.3389/fmars.2015.00008 10.3389/fmars.2018.00149 10.1002/widm.1114 10.18637/jss.v077.i01 10.3390/w7084088 10.1007/s11269-017-1589-6 10.1007/s11269-017-1774-7 10.3390/w10091123 10.1051/proc/201760144 10.1007/s11069-017-2934-z 10.5194/tc-12-3617-2018 10.1016/j.watres.2017.10.032 10.5194/tc-12-1307-2018 10.3390/w10091155 10.1029/2017JD027992 10.1162/neco.1996.8.7.1341 10.1016/j.csda.2013.04.010 10.1029/2017JD027824 10.1029/2011JG001708 10.3390/w10070894 10.1007/s11749-016-0481-7 10.1007/s11269-016-1423-6 10.1002/rra.1247 10.1002/widm.1072 10.1016/j.watres.2017.07.035 10.1162/neco.1997.9.7.1545 10.1016/j.datak.2013.07.002 10.1007/s00027-017-0557-9 10.1002/sam.11196 10.3390/w10050599 10.1002/2017JC013631 10.1002/widm.8 10.5194/hess-22-1371-2018 10.1093/bioinformatics/btn356 10.1002/bimj.201700129 10.1214/10-STS330 10.1016/j.desal.2017.11.044 10.1051/kmae/2011031 10.1007/s00027-013-0306-7 10.1126/sciadv.1700768 10.1080/00031305.2015.1005128 10.1029/2017JC013638 10.5194/hess-21-1611-2017 10.1029/2018WR022606 10.1017/S0376892916000576 10.1002/rra.2838 10.1016/j.jhydrol.2015.10.038 10.1186/1471-2105-11-110 10.1093/bib/bbu012 10.1007/s11269-015-1034-7 10.1002/jgrd.50304 10.1016/j.watres.2014.01.013 10.1186/1471-2105-8-25 10.1016/j.advwatres.2018.01.003 10.1016/j.ejrh.2018.04.002 10.1016/j.jhydrol.2016.06.027 10.1029/2017WR022216 10.1007/978-1-4614-6849-3 10.1214/15-AOS1321 10.3390/w10101347 10.1007/s00477-016-1369-5 10.1002/2014WR015634 10.5194/hess-12-603-2008 10.1029/2018WR022681 10.1198/106186006X133933 10.1073/pnas.1800256115 10.1016/j.jhydrol.2011.12.016 10.3390/w10091239 10.3390/w10070824 10.1080/01621459.2017.1319839 10.1177/0309133312444943 10.5194/tc-12-343-2018 10.1002/2016WR020301 10.1007/s10533-017-0310-4 10.1016/j.jmva.2015.06.009 10.1016/j.jhydrol.2014.08.053 10.2134/jeq2017.08.0329 10.3389/fmars.2017.00335 10.1016/j.jhydrol.2017.04.035 10.1002/rra.2656 10.1002/2017WR021119 10.3390/w9110833 10.7287/peerj.preprints.26693v3 10.1007/BF00058655 10.3390/w10060676 10.1007/s11269-017-1660-3 10.1007/978-0-387-84858-7 10.5194/tc-12-2437-2018 10.1002/rra.3153 10.1080/1573062X.2016.1148178 10.1109/ICCV.2011.6126429 10.1016/j.watres.2005.01.001 10.1080/20964471.2018.1435072 10.1016/j.watres.2016.08.035 10.1073/pnas.1711236115 10.1890/07-0539.1 10.1016/j.envsoft.2016.08.006 10.1214/19-AOAS1247 10.3389/fmars.2016.00252 10.5194/adgeo-45-201-2018 10.1016/j.ygeno.2012.04.003 10.1016/j.patrec.2012.04.003 10.1007/s11269-018-1977-6 10.1007/s11269-017-1865-5 10.1080/02626667.2018.1469756 10.1016/j.jhydrol.2014.05.049 10.1109/TPAMI.2009.23 10.1016/j.envsoft.2006.06.008 10.1002/2017WR020482 10.1007/s11069-016-2579-3 10.1002/wrcr.20339 10.5194/hess-18-3393-2014 10.1002/wrcr.20308 10.1007/s11069-015-1842-3 10.3390/w10070888 10.1186/s40562-018-0111-1 10.1214/18-AOS1709 10.1016/j.jhydrol.2015.10.039 10.1002/2017WR020784 10.1111/1752-1688.12555 10.1016/j.jhydrol.2018.04.042 10.1126/science.aal4321 10.1016/j.jhydrol.2016.03.017 10.2202/1544-6115.1691 10.2166/hydro.2008.015 10.1029/2009WR008898 10.1080/1573062X.2018.1459748 10.1029/2018JD028447 10.1109/TCBB.2011.46 10.1002/widm.14 10.1109/ICCVW.2011.6130407 10.1016/j.agwat.2016.11.003 10.3390/w10111519 10.1002/wics.1346 10.1029/2018WR023378 10.3390/w7041437 10.1017/S0376892913000428 10.1029/2017JD027623 10.1561/0600000035 10.1016/j.jhydrol.2012.02.031 10.1016/j.jhydrol.2004.06.020 10.5194/hess-15-1895-2011 10.1002/2016WR019831 10.1002/rra.2581 10.1002/9781119960003 10.1016/j.csda.2015.10.005 10.1016/j.jhydrol.2018.09.043 10.1016/j.jhydrol.2018.07.004 10.1080/15481603.2017.1419602 10.3390/s18082674 10.5194/hess-17-2685-2013 10.1016/j.agwat.2012.07.003 10.1007/s11269-015-1016-9 10.1016/j.advwatres.2017.12.009 10.1029/2017JC013404 10.1007/s11069-014-1270-9 10.1002/2012WR012660 10.1109/TPAMI.2006.211 10.1002/hyp.13224 10.1016/j.jhydrol.2018.04.024 10.1016/j.jhydrol.2015.02.025 10.1007/s10653-013-9510-6 10.1007/s11069-015-1893-5 10.1002/widm.12 10.1002/2017WR020390 10.1016/j.spl.2018.02.015 10.1016/j.jhydrol.2013.07.009 10.1016/j.patrec.2005.08.011 10.1002/rra.3029 10.1002/2017JG003968 10.3390/w10081019 10.1186/1471-2105-7-3 10.1016/j.jhydrol.2018.07.015 10.1016/j.jhydrol.2011.08.002 10.1002/bimj.201700067 10.1016/j.bdr.2017.07.003 10.3390/w10121765 10.5194/hess-22-4097-2018 10.1002/widm.1249 10.1002/2017WR020630 10.1007/s11069-018-3427-4 10.1093/bioinformatics/btw765 10.1093/bib/bbr053 10.5194/hess-20-2589-2016 10.1002/2016WR020197 10.1007/s13171-018-0133-y 10.1002/bimj.201300226 10.1016/j.agwat.2017.08.003 10.1109/ICCVW.2009.5457447 10.1111/1752-1688.12685 10.1023/A:1007607513941 10.1016/j.asoc.2014.02.002 10.1109/TIT.2009.2025558 10.1007/s11269-018-1944-2 10.1177/030913330102500104 10.1016/j.jhydrol.2016.07.048 10.1016/j.patcog.2010.08.011 10.1029/2018JD028422 10.1007/s00027-011-0194-7 10.3391/ai.2018.13.4.09 10.1214/10-AOAS367 10.1007/s11069-017-3104-z 10.1007/s11069-017-3043-8 10.1016/j.jhydrol.2013.11.007 10.1017/S0376892911000658 10.1080/09715010.2009.10514968 10.1016/j.jhydrol.2014.11.012 10.1016/j.envsoft.2010.02.003 10.1109/34.709601 10.1002/2016JD025154 10.3390/a10040114 10.1198/tast.2009.08199 10.1002/rra.2906 10.1016/j.watres.2010.05.019 10.5194/tc-10-257-2016 10.5194/hess-17-2669-2013 10.1080/10618600.2017.1384734 10.1029/2004WR003094 10.5194/hess-18-4467-2014 10.1029/2018JG004727 10.1007/s10994-006-6226-1 10.1016/j.jhydrol.2017.06.020 10.1214/ss/1009213726 10.1017/CBO9781316576533 10.1037/a0016973 10.1029/2011WR011088 10.1016/j.jhydrol.2015.11.009 10.1016/j.jhydrol.2018.09.055 10.1061/(ASCE)WR.1943-5452.0000969 10.1016/j.jhydrol.2017.08.013 10.1016/j.jhydrol.2015.06.008 10.1016/j.ymeth.2016.08.014 10.3390/w10010056 10.3390/w7126656 10.1016/j.jhydrol.2018.01.044 10.1002/2015WR017394 10.1016/j.jhydrol.2010.04.005 10.5194/hess-19-2859-2015 10.1029/2017WR021749 10.1029/2008WR007474 10.1002/rra.2917 10.1051/kmae/2017032 10.1002/2016JF004180 10.1016/j.patrec.2010.03.014 10.5268/IW-5.2.808 10.3390/w10040391 10.3390/w10101460 10.1007/s10040-018-1767-5 10.1007/978-1-4614-7138-7 10.1016/j.jhydrol.2016.04.025 10.1186/1471-2105-9-307 10.1021/jm4004285 10.1002/rra.2998 10.1007/s00027-018-0592-1
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References	Redo (ref_262) 2012; 39 Segal (ref_97) 2011; 1 Brentan (ref_157) 2018; 15 Meinshausen (ref_85) 2006; 7 Genuer (ref_82) 2010; 31 (ref_193) 2017; 53 ref_99 ref_130 Kohestani (ref_212) 2015; 79 Biau (ref_68) 2012; 13 Boulesteix (ref_52) 2015; 69 ref_96 Basu (ref_102) 2018; 115 ref_135 ref_95 ref_134 Zhao (ref_332) 2018; 563 Nelson (ref_242) 2018; 123 Brunner (ref_158) 2018; 32 Phan (ref_34) 2016; 85 Zhao (ref_330) 2012; 420–421 Schnier (ref_277) 2014; 517 Wager (ref_92) 2018; 113 Papacharalampous (ref_132) 2018; 45 Roubeix (ref_269) 2017; 418 Chenar (ref_167) 2018; 128 Leasure (ref_214) 2016; 32 Berryman (ref_148) 2018; 123 Cox (ref_49) 2018; 136 Winham (ref_126) 2013; 6 Tesoriero (ref_300) 2017; 53 ref_248 Yamazaki (ref_323) 2013; 118 ref_129 Dubinsky (ref_172) 2016; 105 ref_120 ref_123 Snelder (ref_252) 2014; 18 Schwarz (ref_278) 2013; 35 Rowden (ref_270) 2017; 4 Markonis (ref_228) 2018; 113 Athey (ref_89) 2019; 47 Giglio (ref_190) 2018; 123 Loos (ref_221) 2011; 409 Zscheischler (ref_338) 2016; 121 Thielen (ref_263) 2014; 18 Castelletti (ref_164) 2010; 46 Singh (ref_288) 2018; 54 Erechtchoukova (ref_173) 2016; 30 Wang (ref_314) 2015; 527 Kasiviswanathan (ref_35) 2017; 31 Rossi (ref_268) 2015; 531 Wright (ref_131) 2017; 77 Gudmundsson (ref_194) 2015; 19 Boulesteix (ref_83) 2015; 16 Yaseen (ref_32) 2015; 530 ref_77 ref_75 Dawson (ref_21) 2001; 25 Nourani (ref_28) 2014; 514 Schnieders (ref_276) 2013; 118 Shiri (ref_282) 2017; 549 Mahdavi (ref_9) 2018; 55 Sahoo (ref_273) 2018; 32 Zhao (ref_331) 2018; 26 Zimmermann (ref_337) 2014; 50 Bernard (ref_121) 2012; 33 Zhu (ref_335) 2016; 533 Papacharalampous (ref_87) 2018; 5 Snelder (ref_291) 2013; 29 Breiman (ref_1) 2001; 45 Vaughan (ref_309) 2017; 122 ref_147 Maxwell (ref_8) 2018; 39 Seibert (ref_279) 2017; 21 ref_80 Shmueli (ref_44) 2010; 25 Geurts (ref_103) 2006; 63 Gong (ref_192) 2015; 19 Gerlitz (ref_189) 2016; 20 Boulesteix (ref_74) 2012; 13 Olshen (ref_41) 2001; 16 ref_86 ref_143 Trancoso (ref_303) 2016; 535 Menberu (ref_232) 2017; 53 Goodall (ref_247) 2018; 559 Gao (ref_187) 2018; 567 Waugh (ref_317) 2018; 123 Biau (ref_2) 2016; 25 Jain (ref_24) 2009; 15 Boulesteix (ref_45) 2014; 56 Robinson (ref_266) 2018; 32 Miller (ref_235) 2018; 54 ref_213 Lorenz (ref_223) 2018; 123 Hamel (ref_195) 2017; 31 ref_215 ref_218 Afan (ref_33) 2016; 541 Cancela (ref_161) 2017; 183 Thompson (ref_151) 2018; 112 Chen (ref_10) 2011; 1 Heinze (ref_81) 2018; 60 Shchur (ref_280) 2017; 44 McManamay (ref_230) 2014; 519 Verikas (ref_70) 2011; 44 Ellis (ref_122) 2012; 93 Addor (ref_137) 2018; 54 Wang (ref_312) 2018; 54 Ishwaran (ref_110) 2011; 4 Zhang (ref_329) 2016; 121 Biau (ref_64) 2008; 9 Maier (ref_25) 2010; 25 Scornet (ref_66) 2016; 146 ref_207 Ishwaran (ref_98) 2008; 3 Ho (ref_61) 1998; 20 Reynolds (ref_265) 2015; 523 ref_201 Wolpert (ref_90) 1996; 8 Shen (ref_37) 2018; 54 Vezza (ref_311) 2014; 76 McGrath (ref_229) 2018; 12 Bui (ref_159) 2016; 540 Lima (ref_219) 2016; 537 Cherkasov (ref_13) 2014; 57 Yang (ref_325) 2017; 53 ref_114 ref_237 ref_119 ref_239 Smith (ref_289) 2010; 44 Rahman (ref_127) 2017; 33 Cabrera (ref_160) 2018; 435 Naghibi (ref_238) 2017; 31 ref_111 Parkhurst (ref_250) 2005; 39 ref_112 Belgiu (ref_7) 2016; 114 Zhou (ref_334) 2015; 79 Zimmermann (ref_336) 2012; 428–429 Aguilera (ref_26) 2011; 26 Bachmair (ref_141) 2016; 20 Scornet (ref_65) 2015; 43 Shah (ref_101) 2014; 15 Reed (ref_168) 2015; 126 Vezza (ref_245) 2013; 409 Afshar (ref_30) 2015; 29 Chen (ref_14) 2011; 8 Qi (ref_259) 2018; 92 Cutler (ref_5) 2007; 88 Lin (ref_220) 2015; 7 Breiman (ref_40) 2001; 16 Olson (ref_246) 2012; 48 Shiri (ref_281) 2018; 561 Umar (ref_306) 2018; 556 Deng (ref_124) 2013; 46 Rodriguez (ref_105) 2006; 28 Loh (ref_19) 2011; 1 ref_108 Booker (ref_155) 2014; 508 ref_107 Santos (ref_275) 2017; 14 Yang (ref_324) 2017; 53 Wanik (ref_315) 2015; 79 ref_109 Wright (ref_318) 2018; 12 Sadler (ref_272) 2018; 559 Francke (ref_180) 2008; 22 Ziegler (ref_17) 2014; 4 Raghavendra (ref_29) 2014; 19 Booker (ref_153) 2012; 434–435 Cadena (ref_243) 2016; 538 Piniewski (ref_256) 2017; 33 Keto (ref_209) 2018; 80 Solomatine (ref_20) 2008; 10 Anderson (ref_138) 2018; 123 Shortridge (ref_283) 2014; 53 Dhungel (ref_170) 2016; 32 Oczkowski (ref_244) 2016; 3 Probst (ref_79) 2018; 18 Ada (ref_136) 2018; 90 Li (ref_217) 2018; 123 Baudron (ref_145) 2013; 499 Boyle (ref_156) 2015; 120 Genuer (ref_48) 2017; 9 Povak (ref_258) 2013; 49 Schubach (ref_91) 2017; 7 Chen (ref_12) 2012; 99 Xiao (ref_320) 2018; 94 Wager (ref_84) 2014; 15 Fullerton (ref_182) 2018; 80 Denil (ref_113) 2013; 28 Tongal (ref_302) 2018; 564 Chipman (ref_94) 2010; 4 Bhuiyan (ref_149) 2018; 22 Bowden (ref_22) 2005; 301 Ozuysal (ref_117) 2010; 32 Behnia (ref_146) 2018; 90 Meador (ref_231) 2012; 28 Amit (ref_60) 1997; 9 Winowiecki (ref_307) 2018; 47 Meinshausen (ref_118) 2010; 4 Yang (ref_326) 2016; 52 Feng (ref_177) 2015; 7 Yan (ref_125) 2013; 66 Diesing (ref_171) 2017; 135 Gmur (ref_191) 2014; 41 Midekisa (ref_234) 2014; 50 Jing (ref_208) 2018; 32 (ref_73) 2009; 63 Fukuda (ref_181) 2013; 116 Tesfa (ref_299) 2009; 45 Snelder (ref_290) 2012; 74 Wang (ref_54) 2016; 111 Salo (ref_274) 2016; 32 Herrera (ref_199) 2010; 387 ref_313 Bond (ref_152) 2017; 53 Fang (ref_174) 2017; 122 Abrahart (ref_27) 2012; 36 ref_319 Hyrenbach (ref_184) 2018; 5 Sidibe (ref_285) 2018; 561 Genuer (ref_67) 2012; 24 Janitza (ref_72) 2016; 96 Wanyama (ref_316) 2018; 123 Donoho (ref_46) 2017; 26 Gage (ref_183) 2015; 29 Sieg (ref_286) 2017; 53 Li (ref_216) 2018; 46 Booker (ref_154) 2018; 54 Strobl (ref_106) 2007; 52 Asim (ref_140) 2017; 85 Hoshino (ref_200) 2017; 4 Taormina (ref_298) 2018; 144 Meyers (ref_233) 2017; 124 Carvalho (ref_163) 2018; 15 Loosvelt (ref_222) 2014; 517 Depecker (ref_116) 2013; 14 Forkuor (ref_139) 2015; 7 Tripoliti (ref_93) 2013; 87 ref_301 Navares (ref_241) 2018; 32 ref_304 Papacharalampous (ref_88) 2019; 33 Rahmati (ref_260) 2017; 31 Fang (ref_175) 2018; 561 Shortridge (ref_284) 2016; 20 Galelli (ref_186) 2013; 49 Galelli (ref_185) 2013; 17 Feng (ref_178) 2017; 193 Jacoby (ref_205) 2015; 5 Lu (ref_224) 2018; 566 Vayatis (ref_115) 2009; 55 Fouad (ref_179) 2018; 17 Gegiuc (ref_188) 2018; 12 Goldstein (ref_11) 2011; 10 (ref_251) 2016; 538 Sagi (ref_18) 2018; 8 Chen (ref_50) 2018; 2 ref_58 Breiman (ref_63) 1996; 24 Denisko (ref_128) 2018; 115 Petty (ref_255) 2018; 54 ref_56 ref_296 Nateghi (ref_240) 2014; 74 ref_53 ref_297 Feng (ref_176) 2014; 519 Amaratunga (ref_104) 2008; 24 Snelder (ref_292) 2013; 17 Veettil (ref_310) 2018; 564 Iorgulescu (ref_42) 2004; 40 Simard (ref_287) 2011; 116 Mehr (ref_36) 2018; 566 ref_59 (ref_206) 2018; 13 Hothorn (ref_100) 2006; 15 Lal (ref_169) 2014; 28 Dyke (ref_144) 2018; 63 Xu (ref_322) 2017; 53 Yu (ref_328) 2017; 552 Athey (ref_55) 2017; 355 Maheu (ref_226) 2016; 32 Zheng (ref_333) 2016; 10 Zhang (ref_38) 2018; 63 ref_166 Carlisle (ref_162) 2010; 26 Mosquera (ref_253) 2018; 32 Boulesteix (ref_51) 2018; 60 Scornet (ref_78) 2017; 60 He (ref_197) 2016; 52 Birkel (ref_150) 2014; 30 Dietterich (ref_62) 2000; 40 Speich (ref_293) 2018; 22 Gislason (ref_6) 2006; 27 Rozema (ref_271) 2017; 4 (ref_203) 2017; 122 Povak (ref_257) 2014; 50 Huang (ref_202) 2018; 123 Ezcurra (ref_204) 2011; 15 Bowden (ref_23) 2005; 301 (ref_69) 2015; 7 Su (ref_295) 2018; 123 Criminisi (ref_15) 2011; 7 ref_196 ref_198 Tyralis (ref_305) 2018; 111 Maloney (ref_227) 2016; 32 Liaw (ref_57) 2002; 2 Dawson (ref_133) 2007; 22 Kim (ref_211) 2016; 45 Parker (ref_249) 2016; 32 Strobl (ref_71) 2009; 14 Bachmair (ref_142) 2012; 16 ref_39 Rossel (ref_267) 2018; 5 Boulesteix (ref_16) 2012; 2 Hapfelmeier (ref_76) 2014; 24 Peters (ref_254) 2008; 12 Kim (ref_210) 2015; 7 Adhikari (ref_264) 2018; 47 Haberlandt (ref_308) 2015; 531 Lutz (ref_225) 2018; 54 ref_47 Xu (ref_321) 2018; 123 ref_43 Stephan (ref_294) 2018; 12 ref_3 Choong (ref_31) 2015; 29 Yao (ref_327) 2017; 553 ref_4 Chen (ref_165) 2017; 31 Mitsopoulos (ref_236) 2017; 88 Rattray (ref_261) 2015; 2
References_xml	– volume: 52 start-page: 483 year: 2007 ident: ref_106 article-title: Unbiased split selection for classification trees based on the Gini index publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2006.12.030 – volume: 3 start-page: 841 year: 2008 ident: ref_98 article-title: Random survival forests publication-title: Ann. Appl. Stat. – volume: 567 start-page: 590 year: 2018 ident: ref_187 article-title: Identifying the dominant controls on macropore flow velocity in soils: A meta-analysis publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.10.044 – volume: 31 start-page: 3844 year: 2017 ident: ref_195 article-title: Predicting dry-season flows with a monthly rainfall–runoff model: Performance for gauged and ungauged catchments publication-title: Hydrol. Process. doi: 10.1002/hyp.11298 – volume: 559 start-page: 192 year: 2018 ident: ref_247 article-title: Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using random forest classification publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.02.009 – volume: 29 start-page: 1267 year: 2015 ident: ref_31 article-title: State-of-the-art for modelling reservoir inflows and management optimization publication-title: Water Resour. Manag. doi: 10.1007/s11269-014-0872-z – volume: 126 start-page: 363 year: 2015 ident: ref_168 article-title: Observations of net soil exchange of CO2 in a dryland show experimental warming increases carbon losses in biocrust soils publication-title: Biogeochemistry doi: 10.1007/s10533-015-0163-7 – volume: 428–429 start-page: 170 year: 2012 ident: ref_336 article-title: Forests and erosion: Insights from a study of suspended-sediment dynamics in an overland flow-prone rainforest catchment publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2012.01.039 – volume: 28 start-page: 5091 year: 2014 ident: ref_169 article-title: Assessing the accuracy of soil and water quality characterization using remote sensing publication-title: Water Resour. Manag. doi: 10.1007/s11269-014-0796-7 – ident: ref_297 doi: 10.3390/w10081049 – volume: 15 start-page: 1625 year: 2014 ident: ref_84 article-title: Confidence intervals for random forests: The Jackknife and the infinitesimal Jackknife publication-title: J. Mach. Learn. Res. – ident: ref_112 doi: 10.1109/ITA.2012.6181810 – volume: 123 start-page: 3231 year: 2018 ident: ref_148 article-title: Estimating soil respiration in a subalpine landscape using point, terrain, climate, and greenness data publication-title: J. Geophys. Res. Biogeosci. doi: 10.1029/2018JG004613 – volume: 20 start-page: 4605 year: 2016 ident: ref_189 article-title: A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-20-4605-2016 – volume: 92 start-page: 1179 year: 2018 ident: ref_259 article-title: Prediction of open stope hangingwall stability using random forests publication-title: Nat. Hazards doi: 10.1007/s11069-018-3246-7 – volume: 123 start-page: 976 year: 2018 ident: ref_316 article-title: Land-use, land-use history and soil type affect soil greenhouse gas fluxes from agricultural landscapes of the East African highlands publication-title: J. Geophys. Res. Biogeosci. doi: 10.1002/2017JG003856 – volume: 45 start-page: 5 year: 2001 ident: ref_1 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 121 start-page: 2186 year: 2016 ident: ref_338 article-title: Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes publication-title: J. Geophys. Res. Biogeosci. doi: 10.1002/2016JG003503 – volume: 114 start-page: 24 year: 2016 ident: ref_7 article-title: Random forest in remote sensing: A review of applications and future directions publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2016.01.011 – volume: 54 start-page: 8558 year: 2018 ident: ref_37 article-title: A trans-disciplinary review of deep learning research and its relevance for water resources scientists publication-title: Water Resour. Res. doi: 10.1029/2018WR022643 – volume: 52 start-page: 8217 year: 2016 ident: ref_197 article-title: Spatial downscaling of precipitation using adaptable random forests publication-title: Water Res. Res. doi: 10.1002/2016WR019034 – volume: 28 start-page: 1359 year: 2012 ident: ref_231 article-title: Relations between altered streamflow variability and fish assemblages in Eastern USA streams publication-title: River Res. Appl. doi: 10.1002/rra.1534 – volume: 120 start-page: 1424 year: 2015 ident: ref_156 article-title: The parametric sensitivity of CAM5′s MJO publication-title: J. Geophys. Res. Atmos. doi: 10.1002/2014JD022507 – volume: 26 start-page: 1376 year: 2011 ident: ref_26 article-title: Bayesian networks in environmental modelling publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2011.06.004 – volume: 118 start-page: 1272 year: 2013 ident: ref_276 article-title: Analyzing the footprints of near-surface aqueous turbulence: An image processing-based approach publication-title: J. Geophys. Res. Oceans doi: 10.1002/jgrc.20102 – volume: 7 start-page: 3531 year: 2015 ident: ref_139 article-title: Modeling flood hazard zones at the sub-district level with the rational model integrated with GIS and remote sensing approaches publication-title: Water doi: 10.3390/w7073531 – volume: 63 start-page: 269 year: 2018 ident: ref_144 article-title: Extracting water-related features using reflectance data and principal component analysis of Landsat images publication-title: Hydrol. Sci. J. doi: 10.1080/02626667.2018.1425802 – volume: 46 start-page: 3483 year: 2013 ident: ref_124 article-title: Gene selection with guided regularized random forest publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2013.05.018 – volume: 4 start-page: 141 year: 2017 ident: ref_200 article-title: Fishers’ perceived objectives of community-based coastal resource management in the Kei Islands, Indonesia publication-title: Front. Mar. Sci. doi: 10.3389/fmars.2017.00141 – volume: 5 start-page: 317 year: 2018 ident: ref_184 article-title: Seabird trophic position across three ocean regions tracks ecosystem differences publication-title: Front. Mar. Sci. doi: 10.3389/fmars.2018.00317 – ident: ref_114 – volume: 32 start-page: 2849 year: 2018 ident: ref_241 article-title: Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid publication-title: Stoch. Environ. Res. Risk Assess. doi: 10.1007/s00477-018-1519-z – volume: 561 start-page: 764 year: 2018 ident: ref_175 article-title: Reference evapotranspiration forecasting based on local meteorological and global climate information screened by partial mutual information publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.04.038 – volume: 46 start-page: 64 year: 2018 ident: ref_216 article-title: Spatiotemporal assessment of forest biomass carbon sinks: The relative roles of forest expansion and growth in Sichuan Province, China publication-title: J. Environ. Qual. doi: 10.2134/jeq2016.07.0261 – volume: 563 start-page: 1009 year: 2018 ident: ref_332 article-title: A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.06.081 – volume: 15 start-page: 150 year: 2018 ident: ref_157 article-title: Water demand time series generation for distribution network modeling and water demand forecasting publication-title: Urban Water J. doi: 10.1080/1573062X.2018.1424211 – ident: ref_77 doi: 10.1007/s11634-016-0270-x – volume: 50 start-page: 2798 year: 2014 ident: ref_257 article-title: Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA publication-title: Water Res. Res. doi: 10.1002/2013WR014203 – volume: 33 start-page: 481 year: 2019 ident: ref_88 article-title: Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes publication-title: Stoch. Environ. Res. Risk Assess. doi: 10.1007/s00477-018-1638-6 – volume: 16 start-page: 3699 year: 2012 ident: ref_142 article-title: Hillslope characteristics as controls of subsurface flow variability publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-16-3699-2012 – volume: 22 start-page: 4892 year: 2008 ident: ref_180 article-title: Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests publication-title: Hydrol. Process. doi: 10.1002/hyp.7110 – volume: 19 start-page: 2409 year: 2015 ident: ref_192 article-title: Multi-objective parameter optimization of common land model using adaptive surrogate modeling publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-19-2409-2015 – volume: 39 start-page: 2784 year: 2018 ident: ref_8 article-title: Implementation of machine-learning classification in remote sensing: An applied review publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2018.1433343 – volume: 7 start-page: 2959 year: 2017 ident: ref_91 article-title: Imbalance-aware machine learning for predicting rare and common disease-associated non-coding variants publication-title: Sci. Rep. doi: 10.1038/s41598-017-03011-5 – volume: 16 start-page: 184 year: 2001 ident: ref_41 article-title: A conversation with Leo Breiman publication-title: Stat. Sci. doi: 10.1214/ss/1009213290 – ident: ref_95 doi: 10.1080/10618600.2019.1677243 – volume: 535 start-page: 581 year: 2016 ident: ref_303 article-title: Linking the Budyko framework and the Dunne diagram publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.02.017 – volume: 111 start-page: 301 year: 2018 ident: ref_305 article-title: On the long-range dependence properties of annual precipitation using a global network of instrumental measurements publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2017.11.010 – volume: 538 start-page: 487 year: 2016 ident: ref_251 article-title: Sources of variation in hydrological classifications: Time scale, flow series origin and classification procedure publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.04.049 – volume: 4 start-page: 266 year: 2010 ident: ref_94 article-title: BART: Bayesian Additive Regression Trees publication-title: Ann. Appl. Stat. doi: 10.1214/09-AOAS285 – volume: 517 start-page: 298 year: 2014 ident: ref_277 article-title: Prediction of regional streamflow frequency using model tree ensembles publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.05.029 – volume: 24 start-page: 21 year: 2014 ident: ref_76 article-title: A new variable importance measure for random forests with missing data publication-title: Stat. Comput. doi: 10.1007/s11222-012-9349-1 – volume: 32 start-page: 1993 year: 2018 ident: ref_158 article-title: Synthetic design hydrographs for ungauged catchments: A comparison of regionalization methods publication-title: Stoch. Environ. Res. Risk Assess. doi: 10.1007/s00477-018-1523-3 – ident: ref_166 doi: 10.3390/w10101372 – volume: 32 start-page: 1709 year: 2016 ident: ref_274 article-title: A multi-scale, hierarchical model to map riparian zones publication-title: River Res. Appl. doi: 10.1002/rra.3019 – volume: 20 start-page: 2611 year: 2016 ident: ref_284 article-title: Machine learning methods for empirical streamflow simulation: A comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-20-2611-2016 – volume: 4 start-page: 115 year: 2011 ident: ref_110 article-title: Random survival forests for high-dimensional data publication-title: Stat. Anal. Data Min. doi: 10.1002/sam.10103 – volume: 4 start-page: 184 year: 2017 ident: ref_271 article-title: Assessing drivers of coastal primary production in Northern Marguerite Bay, Antarctica publication-title: Front. Mar. Sci. doi: 10.3389/fmars.2017.00184 – volume: 79 start-page: 1359 year: 2015 ident: ref_315 article-title: Storm outage modeling for an electric distribution network in Northeastern USA publication-title: Nat. Hazards doi: 10.1007/s11069-015-1908-2 – volume: 45 start-page: 1910 year: 2016 ident: ref_211 article-title: Assessment of carbon stocks in the topsoil using random forest and remote sensing images publication-title: J. Environ. Qual. doi: 10.2134/jeq2016.03.0076 – volume: 556 start-page: 325 year: 2018 ident: ref_306 article-title: Use of multispectral satellite remote sensing to assess mixing of suspended sediment downstream of large river confluences publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2017.11.026 – volume: 47 start-page: 746 year: 2018 ident: ref_307 article-title: Spatial gradients of ecosystem health indicators across a human-impacted semiarid savanna publication-title: J. Environ. Qual. doi: 10.2134/jeq2017.07.0300 – ident: ref_53 doi: 10.1002/bimj.201700243 – volume: 24 start-page: 543 year: 2012 ident: ref_67 article-title: Variance reduction in purely random forests publication-title: J. Nonparametric Stat. doi: 10.1080/10485252.2012.677843 – volume: 301 start-page: 75 year: 2005 ident: ref_22 article-title: Input determination for neural network models in water resources applications. Part 1—Background and methodology publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2004.06.021 – volume: 533 start-page: 343 year: 2016 ident: ref_335 article-title: Applying a weighted random forests method to extract karst sinkholes from LiDAR data publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.12.012 – volume: 514 start-page: 358 year: 2014 ident: ref_28 article-title: Applications of hybrid wavelet–artificial intelligence models in hydrology: A review publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.03.057 – volume: 93 start-page: 156 year: 2012 ident: ref_122 article-title: Gradient forests: Calculating importance gradients on physical predictors publication-title: Ecology doi: 10.1890/11-0252.1 – volume: 2 start-page: 8 year: 2015 ident: ref_261 article-title: Wave exposure as a predictor of benthic habitat distribution on high energy temperate reefs publication-title: Front. Mar. Sci. doi: 10.3389/fmars.2015.00008 – volume: 5 start-page: 149 year: 2018 ident: ref_267 article-title: Effects of sample fixation on specimen identification in biodiversity assemblies based on proteomic data (MALDI-TOF) publication-title: Front. Mar. Sci. doi: 10.3389/fmars.2018.00149 – volume: 15 start-page: 629 year: 2014 ident: ref_101 article-title: Random intersection trees publication-title: J. Mach. Learn. Res. – volume: 4 start-page: 55 year: 2014 ident: ref_17 article-title: Mining data with random forests: Current options for real-world applications publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1114 – volume: 77 start-page: 1 year: 2017 ident: ref_131 article-title: Ranger: A fast implementation of random forests for high dimensional data in C++ and R publication-title: J. Stat. Softw. doi: 10.18637/jss.v077.i01 – volume: 7 start-page: 4088 year: 2015 ident: ref_220 article-title: Uncertainty in various habitat suitability models and its impact on habitat suitability estimates for fish publication-title: Water doi: 10.3390/w7084088 – volume: 31 start-page: 1473 year: 2017 ident: ref_260 article-title: Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models publication-title: Water Resour. Manag. doi: 10.1007/s11269-017-1589-6 – volume: 31 start-page: 4715 year: 2017 ident: ref_165 article-title: Multiple random forests modelling for urban water consumption forecasting publication-title: Water Resour. Manag. doi: 10.1007/s11269-017-1774-7 – ident: ref_207 doi: 10.3390/w10091123 – volume: 60 start-page: 144 year: 2017 ident: ref_78 article-title: Tuning parameters in random forests publication-title: ESAIM Proc. Surv. doi: 10.1051/proc/201760144 – volume: 88 start-page: 1591 year: 2017 ident: ref_236 article-title: A data-driven approach to assess large fire size generation in Greece publication-title: Nat. Hazards doi: 10.1007/s11069-017-2934-z – volume: 12 start-page: 3617 year: 2018 ident: ref_229 article-title: Interannual snow accumulation variability on glaciers derived from repeat, spatially extensive ground-penetrating radar surveys publication-title: Cryosphere doi: 10.5194/tc-12-3617-2018 – volume: 7 start-page: 983 year: 2006 ident: ref_85 article-title: Quantile regression forests publication-title: J. Mach. Learn. Res. – volume: 128 start-page: 20 year: 2018 ident: ref_167 article-title: Development of genetic programming-based model for predicting oyster norovirus outbreak risks publication-title: Water Res. doi: 10.1016/j.watres.2017.10.032 – volume: 12 start-page: 1307 year: 2018 ident: ref_318 article-title: Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery publication-title: Cryosphere doi: 10.5194/tc-12-1307-2018 – ident: ref_319 doi: 10.3390/w10091155 – volume: 123 start-page: 4509 year: 2018 ident: ref_223 article-title: Prospects and caveats of weighting climate models for summer maximum temperature projections over North America publication-title: J. Geophys. Res. Atmos. doi: 10.1029/2017JD027992 – volume: 8 start-page: 1341 year: 1996 ident: ref_90 article-title: The lack of a priori distinctions between learning algorithms publication-title: Neural Comput. doi: 10.1162/neco.1996.8.7.1341 – volume: 66 start-page: 178 year: 2013 ident: ref_125 article-title: Cluster forests publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2013.04.010 – volume: 2 start-page: 18 year: 2002 ident: ref_57 article-title: Classification and regression by randomForest publication-title: R News – volume: 123 start-page: 13219 year: 2018 ident: ref_138 article-title: Uncertainty analysis of simulations of the turn-of-the-century drought in the Western United States publication-title: J. Geophys. Res. Atmos. doi: 10.1029/2017JD027824 – ident: ref_109 – volume: 116 start-page: G04021 year: 2011 ident: ref_287 article-title: Mapping forest canopy height globally with spaceborne lidar publication-title: J. Geophys. Res. Biogeosci. doi: 10.1029/2011JG001708 – ident: ref_218 doi: 10.3390/w10070894 – volume: 25 start-page: 197 year: 2016 ident: ref_2 article-title: A random forest guided tour publication-title: TEST doi: 10.1007/s11749-016-0481-7 – volume: 30 start-page: 4329 year: 2016 ident: ref_173 article-title: Short-term predictions of hydrological events on an urbanized watershed using supervised classification publication-title: Water Resour. Manag. doi: 10.1007/s11269-016-1423-6 – volume: 409 start-page: 7 year: 2013 ident: ref_245 article-title: A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers publication-title: Knowl. Manag. Aquat. Syst. – volume: 26 start-page: 118 year: 2010 ident: ref_162 article-title: Predicting the natural flow regime: Models for assessing hydrological alteration in streams publication-title: River Res. Appl. doi: 10.1002/rra.1247 – volume: 2 start-page: 493 year: 2012 ident: ref_16 article-title: Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1072 – volume: 124 start-page: 67 year: 2017 ident: ref_233 article-title: Short-term forecasting of turbidity in trunk main networks publication-title: Water Res. doi: 10.1016/j.watres.2017.07.035 – volume: 9 start-page: 1545 year: 1997 ident: ref_60 article-title: Shape quantization and recognition with randomized trees publication-title: Neural Comput. doi: 10.1162/neco.1997.9.7.1545 – volume: 87 start-page: 41 year: 2013 ident: ref_93 article-title: Modifications of the construction and voting mechanisms of the Random Forests Algorithm publication-title: Data Knowl. Eng. doi: 10.1016/j.datak.2013.07.002 – volume: 80 start-page: 3 year: 2018 ident: ref_182 article-title: Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change publication-title: Aquat. Sci. doi: 10.1007/s00027-017-0557-9 – volume: 6 start-page: 496 year: 2013 ident: ref_126 article-title: A weighted random forests approach to improve predictive performance publication-title: Stat. Anal. Data Min. doi: 10.1002/sam.11196 – ident: ref_239 doi: 10.3390/w10050599 – volume: 123 start-page: 399 year: 2018 ident: ref_295 article-title: Retrieving temperature anomaly in the global subsurface and deeper ocean from satellite observations publication-title: J. Geophys. Res. Oceans doi: 10.1002/2017JC013631 – volume: 1 start-page: 14 year: 2011 ident: ref_19 article-title: Classification and regression trees publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.8 – volume: 22 start-page: 1371 year: 2018 ident: ref_149 article-title: A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-22-1371-2018 – volume: 24 start-page: 2010 year: 2008 ident: ref_104 article-title: Enriched random forests publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn356 – volume: 60 start-page: 216 year: 2018 ident: ref_51 article-title: On the necessity and design of studies comparing statistical methods publication-title: Biom. J. doi: 10.1002/bimj.201700129 – volume: 25 start-page: 289 year: 2010 ident: ref_44 article-title: To explain or to predict? publication-title: Stat. Sci. doi: 10.1214/10-STS330 – volume: 435 start-page: 77 year: 2018 ident: ref_160 article-title: Wind-driven SWRO desalination prototype with and without batteries: A performance simulation using machine learning models publication-title: Desalination doi: 10.1016/j.desal.2017.11.044 – ident: ref_304 doi: 10.1051/kmae/2011031 – volume: 76 start-page: 1 year: 2014 ident: ref_311 article-title: Modelling habitat requirements of bullhead (Cottus gobio) in Alpine streams publication-title: Aquat. Sci. doi: 10.1007/s00027-013-0306-7 – ident: ref_43 doi: 10.1126/sciadv.1700768 – volume: 69 start-page: 201 year: 2015 ident: ref_52 article-title: A statistical framework for hypothesis testing in real data comparison studies publication-title: Am. Stat. doi: 10.1080/00031305.2015.1005128 – volume: 123 start-page: 4649 year: 2018 ident: ref_202 article-title: Using multibeam backscatter data to investigate sediment-acoustic relationships publication-title: J. Geophys. Res. Oceans doi: 10.1029/2017JC013638 – volume: 21 start-page: 1611 year: 2017 ident: ref_279 article-title: Seasonal forecasting of hydrological drought in the Limpopo basin: A comparison of statistical methods publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-21-1611-2017 – volume: 54 start-page: 8792 year: 2018 ident: ref_137 article-title: A ranking of hydrological signatures based on their predictability in space publication-title: Water Resour. Res. doi: 10.1029/2018WR022606 – volume: 44 start-page: 191 year: 2017 ident: ref_280 article-title: Monitoring selective logging with Landsat satellite imagery reveals that protected forests in Western Siberia experience greater harvest than non-protected forests publication-title: Environ. Conserv. doi: 10.1017/S0376892916000576 – volume: 32 start-page: 18 year: 2016 ident: ref_214 article-title: Natural flow regimes of the Ozark-Ouachita interior highlands region publication-title: River Res. Appl. doi: 10.1002/rra.2838 – volume: 530 start-page: 829 year: 2015 ident: ref_32 article-title: Artificial intelligence based models for stream-flow forecasting: 2000–2015 publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.10.038 – ident: ref_75 doi: 10.1186/1471-2105-11-110 – volume: 16 start-page: 338 year: 2015 ident: ref_83 article-title: Letter to the Editor: On the term ‘interaction’ and related phrases in the literature on Random Forests publication-title: Brief. Bioinform. doi: 10.1093/bib/bbu012 – volume: 29 start-page: 3877 year: 2015 ident: ref_183 article-title: The influence of land cover, vertical structure, and socioeconomic factors on outdoor water use in a western US city publication-title: Water Resour. Manag. doi: 10.1007/s11269-015-1034-7 – volume: 118 start-page: 2781 year: 2013 ident: ref_323 article-title: Obtaining diverse behaviors in a climate model without the use of flux adjustments publication-title: J. Geophys. Res. Atmos. doi: 10.1002/jgrd.50304 – volume: 53 start-page: 26 year: 2014 ident: ref_283 article-title: Public health and pipe breaks in water distribution systems: Analysis with internet search volume as a proxy publication-title: Water Res. doi: 10.1016/j.watres.2014.01.013 – ident: ref_107 doi: 10.1186/1471-2105-8-25 – volume: 113 start-page: 1 year: 2018 ident: ref_228 article-title: Global estimation of long-term persistence in annual river runoff publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2018.01.003 – volume: 17 start-page: 64 year: 2018 ident: ref_179 article-title: Regional regression models of percentile flows for the contiguous United States: Expert versus data-driven independent variable selection publication-title: J. Hydrol. Reg. Stud. doi: 10.1016/j.ejrh.2018.04.002 – volume: 540 start-page: 317 year: 2016 ident: ref_159 article-title: Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.06.027 – volume: 54 start-page: 4767 year: 2018 ident: ref_225 article-title: Spatial patterns of water age: Using young water fractions to improve the characterization of transit times in contrasting catchments publication-title: Water Res. Res. doi: 10.1029/2017WR022216 – ident: ref_59 doi: 10.1007/978-1-4614-6849-3 – volume: 43 start-page: 1716 year: 2015 ident: ref_65 article-title: Consistency of random forests publication-title: Ann. Stat. doi: 10.1214/15-AOS1321 – ident: ref_248 doi: 10.3390/w10101347 – volume: 31 start-page: 1659 year: 2017 ident: ref_35 article-title: Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models publication-title: Stoch. Environ. Res. Risk Assess. doi: 10.1007/s00477-016-1369-5 – volume: 50 start-page: 8791 year: 2014 ident: ref_234 article-title: Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia publication-title: Water Res. Res. doi: 10.1002/2014WR015634 – volume: 12 start-page: 603 year: 2008 ident: ref_254 article-title: Modelling groundwater-dependent vegetation patterns using ensemble learning publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-12-603-2008 – volume: 54 start-page: 9883 year: 2018 ident: ref_288 article-title: The relative influence of storm and landscape characteristics on shallow groundwater responses in forested headwater catchments publication-title: Water Res. Res. doi: 10.1029/2018WR022681 – volume: 15 start-page: 651 year: 2006 ident: ref_100 article-title: Unbiased recursive partitioning: A conditional inference framework publication-title: J. Comput. Graph. Stat. doi: 10.1198/106186006X133933 – volume: 115 start-page: 1690 year: 2018 ident: ref_128 article-title: Classification and interaction in random forests publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1800256115 – volume: 420–421 start-page: 292 year: 2012 ident: ref_330 article-title: Recognition of key regions for restoration of phytoplankton communities in the Huai River basin, China publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2011.12.016 – ident: ref_213 doi: 10.3390/w10091239 – ident: ref_296 doi: 10.3390/w10070824 – volume: 113 start-page: 1228 year: 2018 ident: ref_92 article-title: Estimation and inference of heterogeneous treatment effects using random forests publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.2017.1319839 – volume: 36 start-page: 480 year: 2012 ident: ref_27 article-title: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting publication-title: Prog. Phys. Geogr. Earth Environ. doi: 10.1177/0309133312444943 – volume: 12 start-page: 343 year: 2018 ident: ref_188 article-title: Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data publication-title: Cryosphere doi: 10.5194/tc-12-343-2018 – volume: 53 start-page: 3446 year: 2017 ident: ref_324 article-title: Multiobjective reservoir operating rules based on cascade reservoir input variable selection method publication-title: Water Resour. Res. doi: 10.1002/2016WR020301 – volume: 135 start-page: 183 year: 2017 ident: ref_171 article-title: Predicting the standing stock of organic carbon in surface sediments of the North–West European continental shelf publication-title: Biogeochemistry doi: 10.1007/s10533-017-0310-4 – volume: 146 start-page: 72 year: 2016 ident: ref_66 article-title: On the asymptotics of random forests publication-title: J. Multivar. Anal. doi: 10.1016/j.jmva.2015.06.009 – volume: 28 start-page: 1256 year: 2013 ident: ref_113 article-title: Consistency of online random forests publication-title: Proc. Mach. Learn. Res. – volume: 519 start-page: 1278 year: 2014 ident: ref_230 article-title: Quantifying and generalizing hydrologic responses to dam regulation using a statistical modeling approach publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.08.053 – volume: 47 start-page: 735 year: 2018 ident: ref_264 article-title: Projecting soil organic carbon distribution in central Chile under future climate scenarios publication-title: J. Environ. Qual. doi: 10.2134/jeq2017.08.0329 – volume: 4 start-page: 335 year: 2017 ident: ref_270 article-title: High-resolution habitat suitability models for the conservation and management of vulnerable marine ecosystems on the Louisville seamount chain, South Pacific Ocean publication-title: Front. Mar. Sci. doi: 10.3389/fmars.2017.00335 – volume: 549 start-page: 592 year: 2017 ident: ref_282 article-title: Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2017.04.035 – volume: 30 start-page: 456 year: 2014 ident: ref_150 article-title: Assessing the cumulative impacts of hydropower regulation on the flow characteristics of a large Atlantic salmon river system publication-title: River Res. Appl. doi: 10.1002/rra.2656 – volume: 53 start-page: 8781 year: 2017 ident: ref_152 article-title: Prediction of hydrologic characteristics for ungauged catchments to support hydroecological modeling publication-title: Water Resour. Res. doi: 10.1002/2017WR021119 – ident: ref_143 doi: 10.3390/w9110833 – ident: ref_47 doi: 10.7287/peerj.preprints.26693v3 – volume: 24 start-page: 123 year: 1996 ident: ref_63 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1007/BF00058655 – ident: ref_301 doi: 10.3390/w10060676 – volume: 31 start-page: 2761 year: 2017 ident: ref_238 article-title: Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping publication-title: Water Resour. Manag. doi: 10.1007/s11269-017-1660-3 – ident: ref_58 doi: 10.1007/978-0-387-84858-7 – volume: 12 start-page: 2437 year: 2018 ident: ref_294 article-title: Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: Progress in the ESA climate change initiative publication-title: Cryosphere doi: 10.5194/tc-12-2437-2018 – volume: 33 start-page: 1205 year: 2017 ident: ref_256 article-title: Classification of natural flow regimes in Poland publication-title: River Res. Appl. doi: 10.1002/rra.3153 – volume: 14 start-page: 343 year: 2017 ident: ref_275 article-title: Stochastic data mining tools for pipe blockage failure prediction publication-title: Urban Water J. doi: 10.1080/1573062X.2016.1148178 – ident: ref_99 doi: 10.1109/ICCV.2011.6126429 – volume: 39 start-page: 1354 year: 2005 ident: ref_250 article-title: Indicator bacteria at five swimming beaches—analysis using random forests publication-title: Water Res. doi: 10.1016/j.watres.2005.01.001 – volume: 2 start-page: 86 year: 2018 ident: ref_50 article-title: Recent advance in earth observation big data for hydrology publication-title: Big Earth Data doi: 10.1080/20964471.2018.1435072 – volume: 105 start-page: 56 year: 2016 ident: ref_172 article-title: Microbial source tracking in impaired watersheds using PhyloChip and machine-learning classification publication-title: Water Res. doi: 10.1016/j.watres.2016.08.035 – volume: 115 start-page: 1943 year: 2018 ident: ref_102 article-title: Iterative random forests to discover predictive and stable high-order interactions publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1711236115 – volume: 88 start-page: 2783 year: 2007 ident: ref_5 article-title: Random forests for classification in ecology publication-title: Ecology doi: 10.1890/07-0539.1 – volume: 85 start-page: 98 year: 2016 ident: ref_34 article-title: Applications of Bayesian belief networks in water resource management: A systematic review publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2016.08.006 – ident: ref_96 doi: 10.1214/19-AOAS1247 – volume: 3 start-page: 252 year: 2016 ident: ref_244 article-title: Patterns in stable isotope values of nitrogen and carbon in particulate matter from the Northwest Atlantic continental shelf, from the Gulf of Maine to Cape Hatteras publication-title: Front. Mar. Sci. doi: 10.3389/fmars.2016.00252 – volume: 45 start-page: 201 year: 2018 ident: ref_132 article-title: Evaluation of random forests and Prophet for daily streamflow forecasting publication-title: Adv. Geosci. doi: 10.5194/adgeo-45-201-2018 – volume: 99 start-page: 323 year: 2012 ident: ref_12 article-title: Random forests for genomic data analysis publication-title: Genomics doi: 10.1016/j.ygeno.2012.04.003 – volume: 33 start-page: 1580 year: 2012 ident: ref_121 article-title: Dynamic random forests publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2012.04.003 – volume: 32 start-page: 3087 year: 2018 ident: ref_266 article-title: An advanced calibration method for image analysis in laboratory-scale seawater intrusion problems publication-title: Water Resour. Manag. doi: 10.1007/s11269-018-1977-6 – volume: 32 start-page: 1225 year: 2018 ident: ref_273 article-title: On Predictability of groundwater level in Shallow Wells using satellite observations publication-title: Water Resour. Manag. doi: 10.1007/s11269-017-1865-5 – volume: 63 start-page: 1091 year: 2018 ident: ref_38 article-title: Univariate streamflow forecasting using commonly used data-driven models: Literature review and case study publication-title: Hydrol. Sci. J. doi: 10.1080/02626667.2018.1469756 – volume: 517 start-page: 411 year: 2014 ident: ref_222 article-title: Assessing hydrologic prediction uncertainty resulting from soft land cover classification publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.05.049 – volume: 32 start-page: 448 year: 2010 ident: ref_117 article-title: Fast keypoint recognition using random ferns publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2009.23 – volume: 22 start-page: 1034 year: 2007 ident: ref_133 article-title: HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2006.06.008 – volume: 18 start-page: 1 year: 2018 ident: ref_79 article-title: To tune or not to tune the number of trees in random forest publication-title: J. Mach. Learn. Res. – volume: 53 start-page: 2786 year: 2017 ident: ref_325 article-title: Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information publication-title: Water Resour. Res. doi: 10.1002/2017WR020482 – volume: 85 start-page: 471 year: 2017 ident: ref_140 article-title: Earthquake magnitude prediction in Hindukush region using machine learning techniques publication-title: Nat. Hazards doi: 10.1007/s11069-016-2579-3 – volume: 49 start-page: 4295 year: 2013 ident: ref_186 article-title: Tree-based iterative input variable selection for hydrological modeling publication-title: Water Res. Res. doi: 10.1002/wrcr.20339 – volume: 18 start-page: 3393 year: 2014 ident: ref_252 article-title: The influence of methodological procedures on hydrological classification performance publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-18-3393-2014 – volume: 49 start-page: 3531 year: 2013 ident: ref_258 article-title: Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity publication-title: Water Res. Res. doi: 10.1002/wrcr.20308 – volume: 79 start-page: 291 year: 2015 ident: ref_334 article-title: Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction publication-title: Nat. Hazards doi: 10.1007/s11069-015-1842-3 – ident: ref_198 doi: 10.3390/w10070888 – volume: 5 start-page: 12 year: 2018 ident: ref_87 article-title: One-step ahead forecasting of geophysical processes within a purely statistical framework publication-title: Geosci. Lett. doi: 10.1186/s40562-018-0111-1 – volume: 47 start-page: 1148 year: 2019 ident: ref_89 article-title: Generalized random forests publication-title: Ann. Stat. doi: 10.1214/18-AOS1709 – volume: 531 start-page: 863 year: 2015 ident: ref_308 article-title: A fuzzy rule based metamodel for monthly catchment nitrate fate simulations publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.10.039 – volume: 53 start-page: 6050 year: 2017 ident: ref_286 article-title: Tree-based flood damage modeling of companies: Damage processes and model performance publication-title: Water Res. Res. doi: 10.1002/2017WR020784 – volume: 54 start-page: 55 year: 2018 ident: ref_255 article-title: Streamflow Hydrology Estimate using Machine Learning (SHEM) publication-title: J. Am. Water Resour. Assoc. doi: 10.1111/1752-1688.12555 – volume: 561 start-page: 737 year: 2018 ident: ref_281 article-title: Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.04.042 – ident: ref_123 – volume: 355 start-page: 483 year: 2017 ident: ref_55 article-title: Beyond prediction: Using big data for policy problems publication-title: Science doi: 10.1126/science.aal4321 – volume: 537 start-page: 431 year: 2016 ident: ref_219 article-title: Forecasting daily streamflow using online sequential extreme learning machines publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.03.017 – volume: 10 start-page: 32 year: 2011 ident: ref_11 article-title: Random forests for genetic association studies publication-title: Stat. Appl. Genet. Mol. Biol. doi: 10.2202/1544-6115.1691 – volume: 10 start-page: 3 year: 2008 ident: ref_20 article-title: Data-driven modelling: Some past experiences and new approaches publication-title: J. Hydroinformatics doi: 10.2166/hydro.2008.015 – volume: 46 start-page: W09507 year: 2010 ident: ref_164 article-title: Tree-based reinforcement learning for optimal water reservoir operation publication-title: Water Res. Res. doi: 10.1029/2009WR008898 – volume: 15 start-page: 338 year: 2018 ident: ref_163 article-title: Analysing the importance of variables for sewer failure prediction publication-title: Urban Water J. doi: 10.1080/1573062X.2018.1459748 – volume: 123 start-page: 8674 year: 2018 ident: ref_321 article-title: Evaluating different machine learning methods for upscaling evapotranspiration from Flux Towers to the regional scale publication-title: J. Geophys. Res. Atmos. doi: 10.1029/2018JD028447 – volume: 8 start-page: 1580 year: 2011 ident: ref_14 article-title: Methods for identifying SNP interactions: A review on variations of logic regression, random forest and Bayesian logistic regression publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2011.46 – ident: ref_56 – volume: 1 start-page: 55 year: 2011 ident: ref_10 article-title: The use of classification trees for bioinformatics publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.14 – ident: ref_120 doi: 10.1109/ICCVW.2011.6130407 – volume: 183 start-page: 70 year: 2017 ident: ref_161 article-title: Discrimination of irrigation water management effects in pergola trellis system vineyards using a vegetation and soil index publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2016.11.003 – ident: ref_237 doi: 10.3390/w10111519 – ident: ref_135 – volume: 7 start-page: 137 year: 2015 ident: ref_69 article-title: Variable importance in regression models publication-title: Wiley Interdiscip. Rev. Comput. Stat. doi: 10.1002/wics.1346 – volume: 54 start-page: 6983 year: 2018 ident: ref_154 article-title: Inside or outside: Quantifying extrapolation across river networks publication-title: Water Resour. Res. doi: 10.1029/2018WR023378 – volume: 7 start-page: 1437 year: 2015 ident: ref_177 article-title: Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—A case of Yuyao, China publication-title: Water doi: 10.3390/w7041437 – volume: 41 start-page: 187 year: 2014 ident: ref_191 article-title: Effects of different sampling scales and selection criteria on modelling net primary productivity of Indonesian tropical forests publication-title: Environ. Conserv. doi: 10.1017/S0376892913000428 – volume: 123 start-page: 5618 year: 2018 ident: ref_317 article-title: In situ microphysical observations of the 29–30 May 2012 Kingfisher, OK, Supercell with a balloon-borne video disdrometer publication-title: J. Geophys. Res. Atmos. doi: 10.1029/2017JD027623 – volume: 7 start-page: 81 year: 2011 ident: ref_15 article-title: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning publication-title: Found. Trends Comput. Graph. Vis. doi: 10.1561/0600000035 – volume: 434–435 start-page: 78 year: 2012 ident: ref_153 article-title: Comparing methods for estimating flow duration curves at ungauged sites publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2012.02.031 – volume: 301 start-page: 93 year: 2005 ident: ref_23 article-title: Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2004.06.020 – volume: 14 start-page: 39 year: 2013 ident: ref_116 article-title: Ranking forests publication-title: J. Mach. Learn. Res. – volume: 15 start-page: 1895 year: 2011 ident: ref_204 article-title: Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-15-1895-2011 – volume: 53 start-page: 4084 year: 2017 ident: ref_322 article-title: Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model publication-title: Water Res. Res. doi: 10.1002/2016WR019831 – volume: 29 start-page: 822 year: 2013 ident: ref_291 article-title: Natural Flow Regime classifications are sensitive to definition processes publication-title: River Res. Appl. doi: 10.1002/rra.2581 – volume: 9 start-page: 2015 year: 2008 ident: ref_64 article-title: Consistency of random forests and other averaging classifiers publication-title: J. Mach. Learn. Res. – ident: ref_134 doi: 10.1002/9781119960003 – volume: 96 start-page: 57 year: 2016 ident: ref_72 article-title: Random forest for ordinal responses: Prediction and variable selection publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2015.10.005 – volume: 566 start-page: 643 year: 2018 ident: ref_36 article-title: Genetic programming in water resources engineering: A state-of-the-art review publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.09.043 – volume: 564 start-page: 266 year: 2018 ident: ref_302 article-title: Simulation and forecasting of streamflows using machine learning models coupled with base flow separation publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.07.004 – volume: 55 start-page: 623 year: 2018 ident: ref_9 article-title: Remote sensing for wetland classification: A comprehensive review publication-title: GISci. Remote Sens. doi: 10.1080/15481603.2017.1419602 – ident: ref_4 doi: 10.3390/s18082674 – volume: 17 start-page: 2685 year: 2013 ident: ref_292 article-title: Regionalization of patterns of flow intermittence from gauging station records publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-17-2685-2013 – volume: 116 start-page: 142 year: 2013 ident: ref_181 article-title: Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2012.07.003 – volume: 29 start-page: 3891 year: 2015 ident: ref_30 article-title: State of the art review of ant colony optimization applications in water resource management publication-title: Water Resour. Manag. doi: 10.1007/s11269-015-1016-9 – volume: 112 start-page: 124 year: 2018 ident: ref_151 article-title: Hydrologic responses to restored wildfire regimes revealed by soil moisture-vegetation relationships publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2017.12.009 – volume: 123 start-page: 4280 year: 2018 ident: ref_190 article-title: Estimating oxygen in the Southern Ocean using argo temperature and salinity publication-title: J. Geophys. Res. Oceans doi: 10.1029/2017JC013404 – volume: 74 start-page: 1795 year: 2014 ident: ref_240 article-title: Forecasting hurricane-induced power outage durations publication-title: Nat. Hazards doi: 10.1007/s11069-014-1270-9 – volume: 50 start-page: 1457 year: 2014 ident: ref_337 article-title: Connectivity of overland flow by drainage network expansion in a rain forest catchment publication-title: Water Resour. Res. doi: 10.1002/2012WR012660 – ident: ref_129 – volume: 28 start-page: 1619 year: 2006 ident: ref_105 article-title: Rotation forest: A new classifier ensemble method publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2006.211 – volume: 32 start-page: 2624 year: 2018 ident: ref_253 article-title: Effect of land cover and hydro-meteorological controls on soil water DOC concentrations in a high-elevation tropical environment publication-title: Hydrol. Process. doi: 10.1002/hyp.13224 – volume: 561 start-page: 478 year: 2018 ident: ref_285 article-title: Trend and variability in a new, reconstructed streamflow dataset for West and Central Africa, and climatic interactions, 1950–2005 publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.04.024 – volume: 523 start-page: 768 year: 2015 ident: ref_265 article-title: Modeled intermittency risk for small streams in the Upper Colorado River Basin under climate change publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.02.025 – volume: 35 start-page: 495 year: 2013 ident: ref_278 article-title: A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: Understanding the variability within cities publication-title: Environ. Geochem. Health doi: 10.1007/s10653-013-9510-6 – volume: 79 start-page: 1079 year: 2015 ident: ref_212 article-title: Evaluation of liquefaction potential based on CPT data using random forest publication-title: Nat. Hazards doi: 10.1007/s11069-015-1893-5 – volume: 1 start-page: 80 year: 2011 ident: ref_97 article-title: Multivariate random forests publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.12 – volume: 53 start-page: 6709 year: 2017 ident: ref_193 article-title: Mapping the temporary and perennial character of whole river networks publication-title: Water Res. Res. doi: 10.1002/2017WR020390 – volume: 136 start-page: 111 year: 2018 ident: ref_49 article-title: Big data: Some statistical issues publication-title: Stat. Probab. Lett. doi: 10.1016/j.spl.2018.02.015 – volume: 499 start-page: 303 year: 2013 ident: ref_145 article-title: Identifying the origin of groundwater samples in a multi-layer aquifer system with random forest classification publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.07.009 – volume: 27 start-page: 294 year: 2006 ident: ref_6 article-title: Random forests for land cover classification publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2005.08.011 – volume: 32 start-page: 1827 year: 2016 ident: ref_170 article-title: Potential effects of climate change on ecologically relevant streamflow regimes publication-title: River Res. Appl. doi: 10.1002/rra.3029 – volume: 122 start-page: 3088 year: 2017 ident: ref_174 article-title: Decreased soil cation exchange capacity across Northern China’s grasslands over the last three decades publication-title: J. Geophys. Res. Biogeosci. doi: 10.1002/2017JG003968 – ident: ref_313 doi: 10.3390/w10081019 – ident: ref_80 doi: 10.1186/1471-2105-7-3 – volume: 564 start-page: 294 year: 2018 ident: ref_310 article-title: Sensitivity of drought resilience-vulnerability- exposure to hydrologic ratios in contiguous United States publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.07.015 – volume: 409 start-page: 94 year: 2011 ident: ref_221 article-title: Topographic controls on overland flow generation in a forest – An ensemble tree approach publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2011.08.002 – volume: 60 start-page: 431 year: 2018 ident: ref_81 article-title: Variable selection—A review and recommendations for the practicing statistician publication-title: Biom. J. doi: 10.1002/bimj.201700067 – volume: 9 start-page: 28 year: 2017 ident: ref_48 article-title: Random forests for big data publication-title: Big Data Res. doi: 10.1016/j.bdr.2017.07.003 – ident: ref_201 doi: 10.3390/w10121765 – volume: 22 start-page: 4097 year: 2018 ident: ref_293 article-title: Testing an optimality-based model of rooting zone water storage capacity in temperate forests publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-22-4097-2018 – volume: 8 start-page: e1249 year: 2018 ident: ref_18 article-title: Ensemble learning: A survey publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1249 – volume: 53 start-page: 8327 year: 2017 ident: ref_232 article-title: Changes in pore water quality after peatland restoration: Assessment of a large-scale, replicated before-after-control-impact study in Finland publication-title: Water Res. Res. doi: 10.1002/2017WR020630 – volume: 94 start-page: 833 year: 2018 ident: ref_320 article-title: Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data publication-title: Nat. Hazards doi: 10.1007/s11069-018-3427-4 – volume: 33 start-page: 1407 year: 2017 ident: ref_127 article-title: IntegratedMRF: Random forest-based framework for integrating prediction from different data types publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw765 – volume: 13 start-page: 292 year: 2012 ident: ref_74 article-title: Random forest Gini importance favours SNPs with large minor allele frequency: Impact, sources and recommendations publication-title: Brief. Bioinform. doi: 10.1093/bib/bbr053 – volume: 20 start-page: 2589 year: 2016 ident: ref_141 article-title: A quantitative analysis to objectively appraise drought indicators and model drought impacts publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-20-2589-2016 – volume: 53 start-page: 7316 year: 2017 ident: ref_300 article-title: Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification publication-title: Water Res. Res. doi: 10.1002/2016WR020197 – ident: ref_130 doi: 10.1007/s13171-018-0133-y – volume: 56 start-page: 588 year: 2014 ident: ref_45 article-title: Machine learning versus statistical modeling publication-title: Biom. J. doi: 10.1002/bimj.201300226 – volume: 193 start-page: 163 year: 2017 ident: ref_178 article-title: Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2017.08.003 – ident: ref_111 doi: 10.1109/ICCVW.2009.5457447 – volume: 54 start-page: 1258 year: 2018 ident: ref_235 article-title: A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States publication-title: J. Am. Water Resour. Assoc. doi: 10.1111/1752-1688.12685 – volume: 40 start-page: 139 year: 2000 ident: ref_62 article-title: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization publication-title: Mach. Learn. doi: 10.1023/A:1007607513941 – volume: 19 start-page: 372 year: 2014 ident: ref_29 article-title: Support vector machine applications in the field of hydrology: A review publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.02.002 – volume: 55 start-page: 4316 year: 2009 ident: ref_115 article-title: Tree-based ranking methods publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2009.2025558 – volume: 32 start-page: 2523 year: 2018 ident: ref_208 article-title: Reconstructing monthly ECV global soil moisture with an improved spatial resolution publication-title: Water Resour. Manag. doi: 10.1007/s11269-018-1944-2 – volume: 25 start-page: 80 year: 2001 ident: ref_21 article-title: Hydrological modelling using artificial neural networks publication-title: Prog. Phys. Geogr. Earth Environ. doi: 10.1177/030913330102500104 – volume: 541 start-page: 902 year: 2016 ident: ref_33 article-title: Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.07.048 – volume: 44 start-page: 330 year: 2011 ident: ref_70 article-title: Mining data with random forests: A survey and results of new tests publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2010.08.011 – volume: 123 start-page: 6777 year: 2018 ident: ref_217 article-title: Intercomparison of six upscaling evapotranspiration methods: From site to the satellite pixel publication-title: J. Geophys. Res. Atmos. doi: 10.1029/2018JD028422 – volume: 74 start-page: 45 year: 2012 ident: ref_290 article-title: Can bottom-up procedures improve the performance of stream classifications? publication-title: Aquat. Sci. doi: 10.1007/s00027-011-0194-7 – volume: 13 start-page: 513 year: 2018 ident: ref_206 article-title: Distribution patterns and potential for further spread of three invasive fish species (Neogobius melanostomus, Lepomis gibbosus and Pseudorasbora parva) in Slovakia publication-title: Aquat. Invasions doi: 10.3391/ai.2018.13.4.09 – volume: 4 start-page: 2049 year: 2010 ident: ref_118 article-title: Node harvest publication-title: Ann. Appl. Stat. doi: 10.1214/10-AOAS367 – volume: 90 start-page: 1407 year: 2018 ident: ref_146 article-title: Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada publication-title: Nat. Hazards doi: 10.1007/s11069-017-3104-z – volume: 90 start-page: 237 year: 2018 ident: ref_136 article-title: Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey publication-title: Nat. Hazards doi: 10.1007/s11069-017-3043-8 – volume: 508 start-page: 227 year: 2014 ident: ref_155 article-title: Comparing and combining physically-based and empirically-based approaches for estimating the hydrology of ungauged catchments publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.11.007 – volume: 39 start-page: 122 year: 2012 ident: ref_262 article-title: Impacts of internal and external policies on land change in Uruguay, 2001–2009 publication-title: Environ. Conserv. doi: 10.1017/S0376892911000658 – volume: 15 start-page: 52 year: 2009 ident: ref_24 article-title: Rainfall runoff modelling using neural networks: State-of-the-art and future research needs publication-title: ISH J. Hydraul. Eng. doi: 10.1080/09715010.2009.10514968 – volume: 519 start-page: 3591 year: 2014 ident: ref_176 article-title: CUTOFF: A spatio-temporal imputation method publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.11.012 – volume: 25 start-page: 891 year: 2010 ident: ref_25 article-title: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2010.02.003 – volume: 20 start-page: 832 year: 1998 ident: ref_61 article-title: The random subspace method for constructing decision forests publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.709601 – volume: 121 start-page: 11425 year: 2016 ident: ref_329 article-title: Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data publication-title: J. Geophys. Res. Atmos. doi: 10.1002/2016JD025154 – ident: ref_86 doi: 10.3390/a10040114 – volume: 63 start-page: 308 year: 2009 ident: ref_73 article-title: Variable importance assessment in regression: Linear regression versus random forest publication-title: Am. Stat. doi: 10.1198/tast.2009.08199 – volume: 32 start-page: 896 year: 2016 ident: ref_226 article-title: A classification of stream water temperature regimes in the conterminous USA publication-title: River Res. Appl. doi: 10.1002/rra.2906 – volume: 44 start-page: 4067 year: 2010 ident: ref_289 article-title: Novel application of a statistical technique, random forests, in a bacterial source tracking study publication-title: Water Res. doi: 10.1016/j.watres.2010.05.019 – volume: 10 start-page: 257 year: 2016 ident: ref_333 article-title: Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: A statistical summary from lidar data publication-title: Cryosphere doi: 10.5194/tc-10-257-2016 – volume: 17 start-page: 2669 year: 2013 ident: ref_185 article-title: Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-17-2669-2013 – volume: 26 start-page: 745 year: 2017 ident: ref_46 article-title: 50 years of data science publication-title: J. Comput. Graph. Stat. doi: 10.1080/10618600.2017.1384734 – volume: 40 start-page: W08403 year: 2004 ident: ref_42 article-title: Nonparametric direct mapping of rainfall-runoff relationships: An alternative approach to data analysis and modeling? publication-title: Water Resour. Res. doi: 10.1029/2004WR003094 – volume: 18 start-page: 4467 year: 2014 ident: ref_263 article-title: Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: Influence of local factors publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-18-4467-2014 – ident: ref_119 – volume: 123 start-page: 3617 year: 2018 ident: ref_242 article-title: Coupling water and carbon fluxes to constrain estimates of transpiration: The TEA algorithm publication-title: J. Geophys. Res. Biogeosci. doi: 10.1029/2018JG004727 – volume: 63 start-page: 3 year: 2006 ident: ref_103 article-title: Extremely randomized trees publication-title: Mach. Learn. doi: 10.1007/s10994-006-6226-1 – volume: 552 start-page: 92 year: 2017 ident: ref_328 article-title: Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2017.06.020 – volume: 16 start-page: 199 year: 2001 ident: ref_40 article-title: Statistical modeling: The two cultures publication-title: Stat. Sci. doi: 10.1214/ss/1009213726 – ident: ref_3 doi: 10.1017/CBO9781316576533 – volume: 14 start-page: 323 year: 2009 ident: ref_71 article-title: An introduction to recursive partitioning: Rationale, application and characteristics of classification and regression trees, bagging and random forests publication-title: Psychol. Methods doi: 10.1037/a0016973 – volume: 48 start-page: W02504 year: 2012 ident: ref_246 article-title: Predicting natural base-flow stream water chemistry in the western United States publication-title: Water Res. Res. doi: 10.1029/2011WR011088 – volume: 531 start-page: 992 year: 2015 ident: ref_268 article-title: Environmental conditions of boreal springs explained by capture zone characteristics publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.11.009 – volume: 13 start-page: 1063 year: 2012 ident: ref_68 article-title: Analysis of a random forests model publication-title: J. Mach. Learn. Res. – volume: 566 start-page: 668 year: 2018 ident: ref_224 article-title: Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.09.055 – volume: 144 start-page: 04018048 year: 2018 ident: ref_298 article-title: Battle of the attack detection algorithms: Disclosing cyber attacks on water distribution networks publication-title: J. Water Resour. Plan. Manag. doi: 10.1061/(ASCE)WR.1943-5452.0000969 – volume: 553 start-page: 508 year: 2017 ident: ref_327 article-title: Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2017.08.013 – volume: 527 start-page: 1130 year: 2015 ident: ref_314 article-title: Flood hazard risk assessment model based on random forest publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.06.008 – volume: 111 start-page: 21 year: 2016 ident: ref_54 article-title: Feature selection methods for big data bioinformatics: A survey from the search perspective publication-title: Methods doi: 10.1016/j.ymeth.2016.08.014 – ident: ref_147 doi: 10.3390/w10010056 – volume: 7 start-page: 6702 year: 2015 ident: ref_210 article-title: Distribution of epilithic diatoms in estuaries of the Korean Peninsula in relation to environmental variables publication-title: Water doi: 10.3390/w7126656 – volume: 559 start-page: 43 year: 2018 ident: ref_272 article-title: Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and random forest publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.01.044 – volume: 52 start-page: 1626 year: 2016 ident: ref_326 article-title: Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme publication-title: Water Resour. Res. doi: 10.1002/2015WR017394 – volume: 387 start-page: 141 year: 2010 ident: ref_199 article-title: Predictive models for forecasting hourly urban water demand publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2010.04.005 – volume: 19 start-page: 2859 year: 2015 ident: ref_194 article-title: Towards observation-based gridded runoff estimates for Europe publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-19-2859-2015 – volume: 54 start-page: 4785 year: 2018 ident: ref_312 article-title: The impact of landscape characteristics on groundwater dissolved organic nitrogen: Insights from machine learning methods and sensitivity analysis publication-title: Water Res. Res. doi: 10.1029/2017WR021749 – volume: 45 start-page: W10438 year: 2009 ident: ref_299 article-title: Modeling soil depth from topographic and land cover attributes publication-title: Water Res. Res. doi: 10.1029/2008WR007474 – volume: 32 start-page: 992 year: 2016 ident: ref_249 article-title: The effects of improved water quality on fish assemblages in a heavily modified large river system publication-title: River Res. Appl. doi: 10.1002/rra.2917 – volume: 418 start-page: 41 year: 2017 ident: ref_269 article-title: Physico-chemical thresholds in the distribution of fish species among French lakes publication-title: Knowl. Manag. Aquat. Syst. doi: 10.1051/kmae/2017032 – volume: 122 start-page: 1901 year: 2017 ident: ref_309 article-title: Near-channel versus watershed controls on sediment rating curves publication-title: J. Geophys. Res. Earth Surf. doi: 10.1002/2016JF004180 – volume: 31 start-page: 2225 year: 2010 ident: ref_82 article-title: Variable selection using random forests publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2010.03.014 – volume: 5 start-page: 187 year: 2015 ident: ref_205 article-title: Dominant factors associated with microcystins in nine midlatitude, maritime lakes publication-title: Inland Waters doi: 10.5268/IW-5.2.808 – ident: ref_215 doi: 10.3390/w10040391 – ident: ref_196 doi: 10.3390/w10101460 – volume: 26 start-page: 2327 year: 2018 ident: ref_331 article-title: Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China publication-title: Hydrogeol. J. doi: 10.1007/s10040-018-1767-5 – ident: ref_39 doi: 10.1007/978-1-4614-7138-7 – volume: 122 start-page: 11045 year: 2017 ident: ref_203 article-title: Automatic cloud-type classification based on the combined use of a sky camera and a ceilometer publication-title: J. Geophys. Res. Atmos. – volume: 538 start-page: 515 year: 2016 ident: ref_243 article-title: Resolving regional frequency analysis of precipitation at large and complex scales using a bottom-up approach: The Latin America and the Caribbean drought Atlas publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.04.025 – ident: ref_108 doi: 10.1186/1471-2105-9-307 – volume: 57 start-page: 4977 year: 2014 ident: ref_13 article-title: QSAR modeling: Where have you been? Where are you going to? publication-title: J. Med. Chem. doi: 10.1021/jm4004285 – volume: 32 start-page: 1428 year: 2016 ident: ref_227 article-title: Predicting thermally events in rivers with a strategy to evaluate management alternatives publication-title: River Res. Appl. doi: 10.1002/rra.2998 – volume: 80 start-page: 41 year: 2018 ident: ref_209 article-title: Interactions between environmental factors and vertical extension of helophyte zones in lakes in Finland publication-title: Aquat. Sci. doi: 10.1007/s00027-018-0592-1
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Snippet	Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However,...
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SubjectTerms	Algorithms Artificial intelligence Big Data Classification computer software Decision trees Feature selection Hydrology Machine learning Science Scientists Software Survival analysis Variables water resources water utilities
Title	A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
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