Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)
Across the globe, landslides have been recognized as one of the most detrimental geological calamities, especially in hilly terrains. However, the correct determination of landslide-prone fields remained a challenging task due to the nonlinear, complex, and inconsistent nature of landslides. Therefo...
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| Veröffentlicht in: | Arabian journal for science and engineering (2011) Jg. 47; H. 6; S. 7367 - 7385 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2022
Springer Nature B.V |
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| ISSN: | 2193-567X, 1319-8025, 2191-4281 |
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| Abstract | Across the globe, landslides have been recognized as one of the most detrimental geological calamities, especially in hilly terrains. However, the correct determination of landslide-prone fields remained a challenging task due to the nonlinear, complex, and inconsistent nature of landslides. Therefore, it is essential to apply methods with superior capabilities in dealing with real-world problems for properly delineating potential landslide zones. Recently, ensemble learning techniques have been drawn intense interest in landslide susceptibility mapping studies due to their distinct advantages. This present work intended to propose natural gradient boosting (NGBoost), a novel member of the ensemble learning family, for modeling landslide susceptibility for Macka County of Trabzon province, Turkey. The predictive performance of NGBoost was compared to ensemble-based machine learning methods, namely random forest (RF) and XGBoost using five accuracy metrics including overall accuracy (OA),
F
1 score, Kappa coefficient, area under curve (AUC) value, and root-mean-square error to evaluate its competence and robustness. Besides, SHAP based on the game theory approach was implemented to interpret the influences of the predisposing factors on the produced model. Results indicated that the NGBoost method utilized for landslide susceptibility mapping problem for the first time had the greatest predictive ability (AUC = 0.898), followed by XGBoost (AUC = 0.871) and RF (AUC = 0.863), and outperformed the XGBoost and RF by approximately 6% in terms of OA. McNemar’s statistical significance test results also confirmed the superiority of the proposed NGBoost method over the RF and XGBoost algorithms. |
|---|---|
| AbstractList | Across the globe, landslides have been recognized as one of the most detrimental geological calamities, especially in hilly terrains. However, the correct determination of landslide-prone fields remained a challenging task due to the nonlinear, complex, and inconsistent nature of landslides. Therefore, it is essential to apply methods with superior capabilities in dealing with real-world problems for properly delineating potential landslide zones. Recently, ensemble learning techniques have been drawn intense interest in landslide susceptibility mapping studies due to their distinct advantages. This present work intended to propose natural gradient boosting (NGBoost), a novel member of the ensemble learning family, for modeling landslide susceptibility for Macka County of Trabzon province, Turkey. The predictive performance of NGBoost was compared to ensemble-based machine learning methods, namely random forest (RF) and XGBoost using five accuracy metrics including overall accuracy (OA),
F
1 score, Kappa coefficient, area under curve (AUC) value, and root-mean-square error to evaluate its competence and robustness. Besides, SHAP based on the game theory approach was implemented to interpret the influences of the predisposing factors on the produced model. Results indicated that the NGBoost method utilized for landslide susceptibility mapping problem for the first time had the greatest predictive ability (AUC = 0.898), followed by XGBoost (AUC = 0.871) and RF (AUC = 0.863), and outperformed the XGBoost and RF by approximately 6% in terms of OA. McNemar’s statistical significance test results also confirmed the superiority of the proposed NGBoost method over the RF and XGBoost algorithms. Across the globe, landslides have been recognized as one of the most detrimental geological calamities, especially in hilly terrains. However, the correct determination of landslide-prone fields remained a challenging task due to the nonlinear, complex, and inconsistent nature of landslides. Therefore, it is essential to apply methods with superior capabilities in dealing with real-world problems for properly delineating potential landslide zones. Recently, ensemble learning techniques have been drawn intense interest in landslide susceptibility mapping studies due to their distinct advantages. This present work intended to propose natural gradient boosting (NGBoost), a novel member of the ensemble learning family, for modeling landslide susceptibility for Macka County of Trabzon province, Turkey. The predictive performance of NGBoost was compared to ensemble-based machine learning methods, namely random forest (RF) and XGBoost using five accuracy metrics including overall accuracy (OA), F1 score, Kappa coefficient, area under curve (AUC) value, and root-mean-square error to evaluate its competence and robustness. Besides, SHAP based on the game theory approach was implemented to interpret the influences of the predisposing factors on the produced model. Results indicated that the NGBoost method utilized for landslide susceptibility mapping problem for the first time had the greatest predictive ability (AUC = 0.898), followed by XGBoost (AUC = 0.871) and RF (AUC = 0.863), and outperformed the XGBoost and RF by approximately 6% in terms of OA. McNemar’s statistical significance test results also confirmed the superiority of the proposed NGBoost method over the RF and XGBoost algorithms. |
| Author | Teke, Alihan Kavzoglu, Taskin |
| Author_xml | – sequence: 1 givenname: Taskin orcidid: 0000-0002-9779-3443 surname: Kavzoglu fullname: Kavzoglu, Taskin email: kavzoglu@gtu.edu.tr organization: Departments of Geomatics Engineering, Gebze Technical University – sequence: 2 givenname: Alihan surname: Teke fullname: Teke, Alihan organization: Departments of Geomatics Engineering, Gebze Technical University |
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| Cites_doi | 10.1080/19475705.2016.1255667 10.1002/widm.1249 10.1080/10106049.2018.1516248 10.1080/10106049.2021.1892210 10.1016/j.jafrearsci.2016.02.019 10.1007/s10115-013-0679-x 10.1596/0-8213-5930-4 10.30991/ijmlnce.2020v04i01.002 10.1016/B978-0-12-811318-9.00033-8 10.1007/s10346-013-0391-7 10.1007/s12665-012-1842-5 10.1007/s00254-006-0435-6 10.1016/S0013-7952(01)00093-X 10.1007/s11069-012-0218-1 10.1007/s11704-019-8208-z 10.1007/s42452-020-3060-1 10.3923/jas.2008.910.921 10.1016/j.catena.2017.11.022 10.4135/9781412983433 10.1016/j.geomorph.2004.06.010 10.1080/13658816.2020.1808897 10.1007/s10346-021-01693-7 10.1155/2020/8830661 10.1016/j.cageo.2016.10.001 10.1007/s11069-005-4669-5 10.1007/978-3-540-69970-5_30 10.1007/s00254-007-0882-8 10.1016/j.cageo.2008.08.007 10.1007/s10346-020-01580-7 10.1007/s00254-006-0256-7 10.1007/s10346-007-0088-x 10.1016/j.catena.2011.01.014 10.1080/10106049.2020.1831623 10.3390/rs13234776 10.1016/j.clay.2011.01.015 10.1007/978-3-319-77377-3_13 10.1016/j.enggeo.2015.04.004 10.1016/j.aei.2020.101201 10.1016/j.enggeo.2004.06.001 10.3390/ijgi9090553 10.1007/978-3-319-55342-9_8 10.1007/s11069-020-04371-4 10.1016/j.scitotenv.2020.137231 10.14481/jkges.2016.17.4.17 10.1023/A:1010933404324 10.1155/2012/974638 10.1016/j.cageo.2020.104445 10.1016/j.enggeo.2005.07.011 10.1080/19475705.2017.1407368 10.1016/j.catena.2020.104805 10.1016/j.geomorph.2010.05.009 10.1007/s002540050348 10.1016/j.cageo.2012.08.023 10.1016/j.scitotenv.2019.01.221 10.5194/isprs-archives-XLII-3-W4-295-2018 10.1016/j.cageo.2011.04.012 10.1109/ACCESS.2019.2923640 10.1089/ees.2006.0161 10.1016/S0169-555X(99)00078-1 10.3390/ijgi9020114 10.5194/nhess-10-623-2010 10.3390/app11114993 10.1007/s11069-014-1506-8 10.1007/s10346-016-0769-4 10.3390/ijgi8120545 10.1016/j.catena.2016.06.004 10.1080/10106049.2016.1170892 10.3390/ATMOS11080823 10.1016/j.geomorph.2006.10.036 10.3390/rs12111737 10.1016/j.geomorph.2008.02.011 10.3390/app9050942 10.1016/j.enggeo.2004.10.004 10.1145/2939672.2939785 10.1038/s42256-019-0138-9 |
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| References | Guzzetti, Carrara, Cardinali, Reichenbach (CR24) 1999; 31 Görüm, Fidan (CR7) 2021; 18 Pradhan (CR25) 2013; 51 Pham, Nguyen-Thoi, Qi, Phong, Dou, Ho, Le, Prakash (CR40) 2020; 195 Dou, Yunus, Tien Bui, Merghadi, Sahana, Zhu, Chen, Khosravi, Yang, Pham (CR43) 2019; 662 Yilmaz (CR28) 2009; 35 Kavzoglu, Colkesen, Sahin (CR48) 2019; 50 Safaei, Omar, Huat, Yousof, Ghiasi (CR3) 2011; 16 Kocaman, Tavus, Nefeslioglu, Karakas, Gokceoglu (CR61) 2020; 2020 Gómez, Kavzoglu (CR33) 2005; 1–2 Peduzzi (CR50) 2010; 10 Akinci, Kilicoglu, Dogan (CR14) 2020; 9 Sahin (CR65) 2020; 2 CR71 CR70 Chakraborty, Elhegazy, Elzarka, Gutierrez (CR67) 2020; 46 Ayalew, Yamagishi (CR78) 2005; 65 Kutlug Sahin, Ipbuker, Kavzoglu (CR13) 2017; 32 Sahin, Colkesen, Kavzoglu (CR60) 2020; 35 Sagi, Rokach (CR38) 2018; 8 Pourghasemi, Rahmati (CR32) 2018; 162 Daǧ, Bulut (CR83) 2012; 36 CR5 Pachauri, Gupta, Chander (CR54) 1998; 36 Yao, Tham, Dai (CR29) 2008; 101 Kavzoglu, Sahin, Colkesen (CR18) 2013; 11 Colkesen, Sahin, Kavzoglu (CR12) 2016; 118 Sezer, Nefeslioglu, Osna (CR15) 2017; 98 Štrumbelj, Kononenko (CR72) 2014; 41 Kavzoglu, Samui, Roy, Balas (CR62) 2017 Akgun, Dag, Bulut (CR16) 2008; 54 Dilley, Chen, Deichmann, Lerner-Lam, Arnold, Agwe, Buys, Kjekstad, Lyon, Yetman (CR2) 2005 CR41 Schuster (CR1) 1996; 247 Gokceoglu, Sonmez, Nefeslioglu, Duman, Can (CR19) 2005; 81 Yalcin, Reis, Aydinoglu, Yomralioglu (CR49) 2011 Dutta (CR69) 2020; 4 Hu, Mei, Zhang, Li, Li (CR81) 2021; 105 Kalantar, Ueda, Saeidi, Ahmadi, Halin, Shabani (CR80) 2020; 12 Dai, Lee, Ngai (CR23) 2002; 64 Viet, Lee, Kim (CR51) 2016; 17 Hong, Liu, Zhu (CR75) 2020; 718 Gao, Shan, Hu, Niu, Liu (CR37) 2019; 7 Tsangaratos, Ilia, Hong, Chen, Xu (CR42) 2017; 14 Kocaman, Gokceoglu (CR47) 2018; 42 Wang, Fang, Wang, Peng, Hong (CR76) 2020; 138 Saleem, Enamul Huq, Twumasi, Javed, Sajjad (CR53) 2019; 8 Yanar, Kocaman, Gokceoglu (CR46) 2020; 9 Menard (CR73) 2002 Yalcin (CR10) 2011; 52 Sahin (CR64) 2020 CR56 Ercanoglu, Gokceoglu (CR17) 2004; 75 Lee, Sambath (CR27) 2006; 50 Fang, Wang, Peng, Hong (CR39) 2021; 35 Kalantar, Pradhan, Amir Naghibi, Motevalli, Mansor (CR30) 2018; 9 Kavzoglu, Kutlug Sahin, Colkesen (CR26) 2015; 76 Kjekstad, Highland, Zhou, Ooi, Meng (CR4) 2009 Tien Bui, Pradhan, Lofman, Revhaug (CR31) 2012 Nefeslioglu, Duman, Durmaz (CR52) 2008; 94 Tagil, Jeff (CR55) 2008; 8 Tsangaratos, Ilia (CR79) 2016; 145 Lima, Steger, Glade (CR45) 2021; 18 Dong, Yu, Cao, Shi, Ma (CR36) 2020; 14 Kavzoglu, Teke, Yilmaz (CR35) 2021; 13 Kavzoglu, Kutlug Sahin, Colkesen (CR11) 2015; 192 Lee, Ryu, Kim (CR34) 2007; 4 Park, Kim (CR57) 2019 Pradhan, Jebur, Pradhan (CR20) 2017 Peng, Zhi, Ji, Ji, Tian (CR68) 2020; 11 CR66 Park, Choi, Kim, Kim (CR74) 2013; 68 Akgun, Sezer, Nefeslioglu, Gokceoglu, Pradhan (CR77) 2012; 38 CR63 Arabameri, Chandra Pal, Rezaie, Chakrabortty, Saha, Blaschke, Di Napoli, Ghorbanzadeh, Thi Ngo (CR82) 2021 Yalcin (CR8) 2007; 24 Regmi, Giardino, Vitek (CR22) 2010; 122 Akgün, Bulut (CR9) 2007; 51 Pham, Bui, Dholakia, Prakash, Pham, Mehmood, Le (CR58) 2017; 8 Breiman (CR59) 2001; 45 Can, Kocaman, Gokceoglu (CR44) 2021; 11 Hasekioğulları, Ercanoglu (CR6) 2012; 63 Chen, Liu, Chan (CR21) 2006; 37 6560_CR5 N Saleem (6560_CR53) 2019; 8 A Akgün (6560_CR9) 2007; 51 P Tsangaratos (6560_CR42) 2017; 14 X Hu (6560_CR81) 2021; 105 AK Pachauri (6560_CR54) 1998; 36 A Yalcin (6560_CR49) 2011 T Kavzoglu (6560_CR11) 2015; 192 S Tagil (6560_CR55) 2008; 8 O Sagi (6560_CR38) 2018; 8 T Yanar (6560_CR46) 2020; 9 A Akgun (6560_CR77) 2012; 38 M Dilley (6560_CR2) 2005 LC Chen (6560_CR21) 2006; 37 GD Hasekioğulları (6560_CR6) 2012; 63 B Pradhan (6560_CR25) 2013; 51 H Hong (6560_CR75) 2020; 718 NR Regmi (6560_CR22) 2010; 122 TT Viet (6560_CR51) 2016; 17 A Akgun (6560_CR16) 2008; 54 FC Dai (6560_CR23) 2002; 64 T Kavzoglu (6560_CR26) 2015; 76 6560_CR41 M Ercanoglu (6560_CR17) 2004; 75 H Akinci (6560_CR14) 2020; 9 J Dou (6560_CR43) 2019; 662 S Kocaman (6560_CR61) 2020; 2020 B Kalantar (6560_CR30) 2018; 9 S Kocaman (6560_CR47) 2018; 42 P Tsangaratos (6560_CR79) 2016; 145 EK Sahin (6560_CR64) 2020 S Park (6560_CR57) 2019 6560_CR71 6560_CR70 D Chakraborty (6560_CR67) 2020; 46 EK Sahin (6560_CR60) 2020; 35 I Yilmaz (6560_CR28) 2009; 35 L Breiman (6560_CR59) 2001; 45 E Štrumbelj (6560_CR72) 2014; 41 D Tien Bui (6560_CR31) 2012 H Gómez (6560_CR33) 2005; 1–2 EA Sezer (6560_CR15) 2017; 98 T Peng (6560_CR68) 2020; 11 I Colkesen (6560_CR12) 2016; 118 S Dutta (6560_CR69) 2020; 4 T Kavzoglu (6560_CR62) 2017 Z Fang (6560_CR39) 2021; 35 S Lee (6560_CR27) 2006; 50 S Lee (6560_CR34) 2007; 4 HA Nefeslioglu (6560_CR52) 2008; 94 6560_CR63 6560_CR66 S Park (6560_CR74) 2013; 68 C Gokceoglu (6560_CR19) 2005; 81 M Safaei (6560_CR3) 2011; 16 P Peduzzi (6560_CR50) 2010; 10 R Can (6560_CR44) 2021; 11 L Ayalew (6560_CR78) 2005; 65 F Guzzetti (6560_CR24) 1999; 31 T Kavzoglu (6560_CR35) 2021; 13 B Kalantar (6560_CR80) 2020; 12 HR Pourghasemi (6560_CR32) 2018; 162 EK Sahin (6560_CR65) 2020; 2 X Dong (6560_CR36) 2020; 14 A Yalcin (6560_CR8) 2007; 24 O Kjekstad (6560_CR4) 2009 B Pradhan (6560_CR20) 2017 X Yao (6560_CR29) 2008; 101 RL Schuster (6560_CR1) 1996; 247 S Daǧ (6560_CR83) 2012; 36 A Yalcin (6560_CR10) 2011; 52 BT Pham (6560_CR58) 2017; 8 X Gao (6560_CR37) 2019; 7 BT Pham (6560_CR40) 2020; 195 A Arabameri (6560_CR82) 2021 Y Wang (6560_CR76) 2020; 138 6560_CR56 S Menard (6560_CR73) 2002 T Kavzoglu (6560_CR18) 2013; 11 P Lima (6560_CR45) 2021; 18 T Görüm (6560_CR7) 2021; 18 E Kutlug Sahin (6560_CR13) 2017; 32 T Kavzoglu (6560_CR48) 2019; 50 |
| References_xml | – volume: 8 start-page: 649 year: 2017 end-page: 671 ident: CR58 article-title: A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS publication-title: Geom. Nat. Hazards Risk. doi: 10.1080/19475705.2016.1255667 – ident: CR70 – volume: 8 start-page: e1249, 1–18 year: 2018 ident: CR38 article-title: Ensemble learning: a survey publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1249 – volume: 35 start-page: 341 year: 2020 end-page: 363 ident: CR60 article-title: A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping publication-title: Geocarto Int. doi: 10.1080/10106049.2018.1516248 – volume: 16 start-page: 1619 year: 2011 end-page: 1650 ident: CR3 article-title: Deterministic rainfall induced landslide approaches, advantage and limitation publication-title: Electron. J. Geotech. Eng. – year: 2021 ident: CR82 article-title: Decision tree based ensemble machine learning approaches for landslide susceptibility mapping publication-title: Geocarto Int. doi: 10.1080/10106049.2021.1892210 – volume: 118 start-page: 53 year: 2016 end-page: 64 ident: CR12 article-title: Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression publication-title: J. African Earth Sci. doi: 10.1016/j.jafrearsci.2016.02.019 – volume: 41 start-page: 647 year: 2014 end-page: 665 ident: CR72 article-title: Explaining prediction models and individual predictions with feature contributions publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-013-0679-x – year: 2005 ident: CR2 publication-title: Natural Disaster Hotspots: A Global Risk Analysis doi: 10.1596/0-8213-5930-4 – volume: 4 start-page: 12 year: 2020 end-page: 20 ident: CR69 article-title: Revealing brain tumor using cross-validated ngboost classifier publication-title: Int. J. Mach. Learn. Netw. Collab. Eng. doi: 10.30991/ijmlnce.2020v04i01.002 – start-page: 607 year: 2017 end-page: 619 ident: CR62 article-title: Object-oriented random forest for high resolution land cover mapping using quickbird-2 ımagery publication-title: Handbook of Neural Computation doi: 10.1016/B978-0-12-811318-9.00033-8 – volume: 11 start-page: 425 year: 2013 end-page: 439 ident: CR18 article-title: Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression publication-title: Landslides doi: 10.1007/s10346-013-0391-7 – volume: 68 start-page: 1443 year: 2013 end-page: 1464 ident: CR74 article-title: Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea publication-title: Environ. Earth Sci. doi: 10.1007/s12665-012-1842-5 – volume: 51 start-page: 1377 year: 2007 end-page: 1387 ident: CR9 article-title: GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region publication-title: Environ. Geol. doi: 10.1007/s00254-006-0435-6 – volume: 64 start-page: 65 year: 2002 end-page: 87 ident: CR23 article-title: Landslide risk assessment and management: an overview publication-title: Eng. Geol. doi: 10.1016/S0013-7952(01)00093-X – volume: 247 start-page: 12 year: 1996 end-page: 35 ident: CR1 article-title: Socioeconomic significance of landslides publication-title: Spec. Rep. Natl. Res. Counc. Transp. Res. Board. – volume: 63 start-page: 1157 year: 2012 end-page: 1179 ident: CR6 article-title: A new approach to use AHP in landslide susceptibility mapping: A case study at Yenice (Karabuk, NW Turkey) publication-title: Nat. Hazards. doi: 10.1007/s11069-012-0218-1 – volume: 14 start-page: 241 year: 2020 end-page: 258 ident: CR36 article-title: A survey on ensemble learning publication-title: Front. Comput. Sci. doi: 10.1007/s11704-019-8208-z – volume: 2 start-page: 1 year: 2020 end-page: 17 ident: CR65 article-title: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest publication-title: SN Appl. Sci. doi: 10.1007/s42452-020-3060-1 – volume: 8 start-page: 910 year: 2008 end-page: 921 ident: CR55 article-title: GIS-based automated landform classification and topographic, landcover and geologic attributes of landforms around the Yazoren Polje, Turkey publication-title: J. Appl. Sci. doi: 10.3923/jas.2008.910.921 – volume: 162 start-page: 177 year: 2018 end-page: 192 ident: CR32 article-title: Prediction of the landslide susceptibility: Which algorithm, which precision? publication-title: CATENA doi: 10.1016/j.catena.2017.11.022 – year: 2002 ident: CR73 publication-title: Applied Logistic Regression Analysis: Sage University Series on Quantitative Applications in the Social Sciences doi: 10.4135/9781412983433 – volume: 65 start-page: 15 year: 2005 end-page: 31 ident: CR78 article-title: The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan publication-title: Geomorphology doi: 10.1016/j.geomorph.2004.06.010 – volume: 35 start-page: 321 year: 2021 end-page: 347 ident: CR39 article-title: A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658816.2020.1808897 – volume: 18 start-page: 3531 year: 2021 end-page: 3546 ident: CR45 article-title: Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria publication-title: Landslides doi: 10.1007/s10346-021-01693-7 – ident: CR71 – volume: 2020 start-page: 8830661 year: 2020 ident: CR61 article-title: Evaluation of floods and landslides triggered by a meteorological catastrophe (Ordu, Turkey, August, 2018) using optical and radar data publication-title: Geofluids doi: 10.1155/2020/8830661 – volume: 98 start-page: 26 year: 2017 end-page: 37 ident: CR15 article-title: An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2016.10.001 – volume: 37 start-page: 209 year: 2006 end-page: 223 ident: CR21 article-title: Integrated community-based disaster management program in taiwan: a case study of shang-an village publication-title: Nat. Hazards. doi: 10.1007/s11069-005-4669-5 – start-page: 573 year: 2009 end-page: 587 ident: CR4 article-title: Economic and social impacts of landslides publication-title: Landslides: Disaster Risk Reduction doi: 10.1007/978-3-540-69970-5_30 – volume: 54 start-page: 1127 year: 2008 end-page: 1143 ident: CR16 article-title: Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models publication-title: Environ. Geol. doi: 10.1007/s00254-007-0882-8 – volume: 36 start-page: 35 year: 2012 end-page: 62 ident: CR83 article-title: An example for preparation of GIS-based landslide susceptibility maps: Çayeli (Rize, NE Türkiye) publication-title: J. Geol. Eng. – volume: 35 start-page: 1125 year: 2009 end-page: 1138 ident: CR28 article-title: Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey) publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2008.08.007 – volume: 18 start-page: 1691 year: 2021 end-page: 1705 ident: CR7 article-title: Spatiotemporal variations of fatal landslides in Turkey publication-title: Landslides doi: 10.1007/s10346-020-01580-7 – volume: 50 start-page: 847 year: 2006 end-page: 855 ident: CR27 article-title: Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models publication-title: Environ. Geol. doi: 10.1007/s00254-006-0256-7 – volume: 4 start-page: 327 year: 2007 end-page: 338 ident: CR34 article-title: Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: Case study of Youngin, Korea publication-title: Landslides doi: 10.1007/s10346-007-0088-x – year: 2011 ident: CR49 article-title: A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey publication-title: CATENA doi: 10.1016/j.catena.2011.01.014 – year: 2020 ident: CR64 article-title: Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping publication-title: Geocarto Int. doi: 10.1080/10106049.2020.1831623 – ident: CR5 – volume: 13 start-page: 4776 year: 2021 ident: CR35 article-title: Shared blocks-based ensemble deep learning for shallow landslide susceptibility mapping publication-title: Remote Sens. doi: 10.3390/rs13234776 – volume: 52 start-page: 11 year: 2011 end-page: 19 ident: CR10 article-title: A geotechnical study on the landslides in the Trabzon Province, NE, Turkey publication-title: Appl. Clay Sci. doi: 10.1016/j.clay.2011.01.015 – volume: 50 start-page: 283 year: 2019 end-page: 301 ident: CR48 article-title: Machine learning techniques in landslide susceptibility mapping: a survey and a case study publication-title: Adv. Nat. Technol. Hazards Res. doi: 10.1007/978-3-319-77377-3_13 – volume: 192 start-page: 101 year: 2015 end-page: 112 ident: CR11 article-title: Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2015.04.004 – volume: 46 year: 2020 ident: CR67 article-title: A novel construction cost prediction model using hybrid natural and light gradient boosting publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2020.101201 – volume: 75 start-page: 229 year: 2004 end-page: 250 ident: CR17 article-title: Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey) publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2004.06.001 – ident: CR66 – volume: 9 start-page: 4993 year: 2020 ident: CR14 article-title: Random forest-based landslide susceptibility mapping in coastal regions of artvin, Turkey publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi9090553 – start-page: 151 year: 2017 end-page: 165 ident: CR20 article-title: Spatial prediction of landslide-prone areas through k-nearest neighbor algorithm and logistic regression model using high resolution airborne laser scanning data publication-title: Laser Scanning Applications in Landslide Assessment doi: 10.1007/978-3-319-55342-9_8 – volume: 105 start-page: 1663 year: 2021 end-page: 1689 ident: CR81 article-title: Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China publication-title: Nat. Hazards. doi: 10.1007/s11069-020-04371-4 – volume: 718 year: 2020 ident: CR75 article-title: Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.137231 – volume: 17 start-page: 17 year: 2016 end-page: 31 ident: CR51 article-title: Shallow landslide assessment considering the ınfluence of vegetation cover publication-title: J. Korean Geoenviron. Soc. doi: 10.14481/jkges.2016.17.4.17 – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: CR59 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – year: 2012 ident: CR31 article-title: Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and naïve bayes models publication-title: Math. Probl. Eng. doi: 10.1155/2012/974638 – volume: 138 year: 2020 ident: CR76 article-title: Comparative study of landslide susceptibility mapping with different recurrent neural networks publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2020.104445 – volume: 81 start-page: 65 year: 2005 end-page: 83 ident: CR19 article-title: The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2005.07.011 – volume: 9 start-page: 49 year: 2018 end-page: 69 ident: CR30 article-title: Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) publication-title: Geom. Nat. Hazards Risk. doi: 10.1080/19475705.2017.1407368 – volume: 195 year: 2020 ident: CR40 article-title: Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping publication-title: CATENA doi: 10.1016/j.catena.2020.104805 – volume: 122 start-page: 25 year: 2010 end-page: 38 ident: CR22 article-title: Assessing susceptibility to landslides: using models to understand observed changes in slopes publication-title: Geomorphology doi: 10.1016/j.geomorph.2010.05.009 – volume: 36 start-page: 325 year: 1998 end-page: 334 ident: CR54 article-title: Landslide zoning in a part of the Garhwal Himalayas publication-title: Environ. Geol. doi: 10.1007/s002540050348 – ident: CR56 – volume: 51 start-page: 350 year: 2013 end-page: 365 ident: CR25 article-title: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2012.08.023 – volume: 662 start-page: 332 year: 2019 end-page: 346 ident: CR43 article-title: Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.01.221 – volume: 42 start-page: 295 year: 2018 end-page: 300 ident: CR47 article-title: Possible contributions of citizen science for landslide hazard assessment publication-title: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. doi: 10.5194/isprs-archives-XLII-3-W4-295-2018 – ident: CR63 – volume: 38 start-page: 23 year: 2012 end-page: 34 ident: CR77 article-title: An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2011.04.012 – volume: 7 start-page: 82512 year: 2019 end-page: 82521 ident: CR37 article-title: An adaptive ensemble machine learning model for intrusion detection publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2923640 – volume: 24 start-page: 821 year: 2007 end-page: 833 ident: CR8 article-title: Environmental impacts of landslides: a case study from East Black Sea region, Turkey publication-title: Environ. Eng. Sci. doi: 10.1089/ees.2006.0161 – volume: 31 start-page: 181 year: 1999 end-page: 216 ident: CR24 article-title: Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy publication-title: Geomorphology doi: 10.1016/S0169-555X(99)00078-1 – volume: 9 start-page: 114 year: 2020 ident: CR46 article-title: Use of Mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey) publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi9020114 – volume: 10 start-page: 623 year: 2010 end-page: 640 ident: CR50 article-title: Landslides and vegetation cover in the 2005 North Pakistan earthquake: a GIS and statistical quantitative approach publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-10-623-2010 – volume: 11 start-page: 4993 year: 2021 ident: CR44 article-title: A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk dam, Turkey publication-title: Appl. Sci. doi: 10.3390/app11114993 – volume: 1–2 start-page: 1 year: 2005 end-page: 27 ident: CR33 article-title: Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin publication-title: Venezuela. Eng. Geol. – volume: 76 start-page: 471 year: 2015 end-page: 496 ident: CR26 article-title: An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district publication-title: Nat. Hazards. doi: 10.1007/s11069-014-1506-8 – volume: 14 start-page: 1091 year: 2017 end-page: 1111 ident: CR42 article-title: Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China publication-title: Landslides doi: 10.1007/s10346-016-0769-4 – volume: 8 start-page: 545 year: 2019 ident: CR53 article-title: Parameters derived from and/or used with digital elevation models (DEMs) for landslide susceptibility mapping and landslide risk assessment: a review publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi8120545 – volume: 145 start-page: 164 year: 2016 end-page: 179 ident: CR79 article-title: Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size publication-title: CATENA doi: 10.1016/j.catena.2016.06.004 – volume: 32 start-page: 956 year: 2017 end-page: 977 ident: CR13 article-title: Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping publication-title: Geocarto Int. doi: 10.1080/10106049.2016.1170892 – volume: 11 start-page: 1 year: 2020 end-page: 17 ident: CR68 article-title: Prediction skill of extended range 2-m maximum air temperature probabilistic forecasts using machine learning post-processing methods publication-title: Atmosphere (Basel). doi: 10.3390/ATMOS11080823 – volume: 94 start-page: 401 year: 2008 end-page: 418 ident: CR52 article-title: Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey) publication-title: Geomorphology doi: 10.1016/j.geomorph.2006.10.036 – volume: 12 start-page: 1 year: 2020 end-page: 23 ident: CR80 article-title: Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data publication-title: Remote Sens. doi: 10.3390/rs12111737 – ident: CR41 – volume: 101 start-page: 572 year: 2008 end-page: 582 ident: CR29 article-title: Landslide susceptibility mapping based on Support Vector Machine: a case study on natural slopes of Hong Kong, China publication-title: Geomorphology doi: 10.1016/j.geomorph.2008.02.011 – year: 2019 ident: CR57 article-title: Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance publication-title: Appl. Sci. doi: 10.3390/app9050942 – volume: 51 start-page: 350 year: 2013 ident: 6560_CR25 publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2012.08.023 – volume: 118 start-page: 53 year: 2016 ident: 6560_CR12 publication-title: J. African Earth Sci. doi: 10.1016/j.jafrearsci.2016.02.019 – volume: 65 start-page: 15 year: 2005 ident: 6560_CR78 publication-title: Geomorphology doi: 10.1016/j.geomorph.2004.06.010 – volume: 4 start-page: 12 year: 2020 ident: 6560_CR69 publication-title: Int. J. Mach. Learn. Netw. Collab. Eng. doi: 10.30991/ijmlnce.2020v04i01.002 – volume: 51 start-page: 1377 year: 2007 ident: 6560_CR9 publication-title: Environ. Geol. doi: 10.1007/s00254-006-0435-6 – volume: 54 start-page: 1127 year: 2008 ident: 6560_CR16 publication-title: Environ. Geol. doi: 10.1007/s00254-007-0882-8 – volume: 76 start-page: 471 year: 2015 ident: 6560_CR26 publication-title: Nat. Hazards. doi: 10.1007/s11069-014-1506-8 – volume: 68 start-page: 1443 year: 2013 ident: 6560_CR74 publication-title: Environ. Earth Sci. doi: 10.1007/s12665-012-1842-5 – volume: 105 start-page: 1663 year: 2021 ident: 6560_CR81 publication-title: Nat. Hazards. doi: 10.1007/s11069-020-04371-4 – ident: 6560_CR41 – volume: 162 start-page: 177 year: 2018 ident: 6560_CR32 publication-title: CATENA doi: 10.1016/j.catena.2017.11.022 – volume: 81 start-page: 65 year: 2005 ident: 6560_CR19 publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2005.07.011 – volume: 1–2 start-page: 1 year: 2005 ident: 6560_CR33 publication-title: Venezuela. Eng. Geol. doi: 10.1016/j.enggeo.2004.10.004 – volume: 138 year: 2020 ident: 6560_CR76 publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2020.104445 – start-page: 607 volume-title: Handbook of Neural Computation year: 2017 ident: 6560_CR62 doi: 10.1016/B978-0-12-811318-9.00033-8 – volume: 24 start-page: 821 year: 2007 ident: 6560_CR8 publication-title: Environ. Eng. Sci. doi: 10.1089/ees.2006.0161 – volume: 11 start-page: 4993 year: 2021 ident: 6560_CR44 publication-title: Appl. Sci. doi: 10.3390/app11114993 – year: 2011 ident: 6560_CR49 publication-title: CATENA doi: 10.1016/j.catena.2011.01.014 – volume: 42 start-page: 295 year: 2018 ident: 6560_CR47 publication-title: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. doi: 10.5194/isprs-archives-XLII-3-W4-295-2018 – volume: 11 start-page: 1 year: 2020 ident: 6560_CR68 publication-title: Atmosphere (Basel). doi: 10.3390/ATMOS11080823 – volume: 63 start-page: 1157 year: 2012 ident: 6560_CR6 publication-title: Nat. Hazards. doi: 10.1007/s11069-012-0218-1 – volume: 98 start-page: 26 year: 2017 ident: 6560_CR15 publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2016.10.001 – volume: 718 year: 2020 ident: 6560_CR75 publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.137231 – volume: 101 start-page: 572 year: 2008 ident: 6560_CR29 publication-title: Geomorphology doi: 10.1016/j.geomorph.2008.02.011 – volume: 36 start-page: 35 year: 2012 ident: 6560_CR83 publication-title: J. Geol. Eng. – volume: 50 start-page: 847 year: 2006 ident: 6560_CR27 publication-title: Environ. Geol. doi: 10.1007/s00254-006-0256-7 – volume: 18 start-page: 3531 year: 2021 ident: 6560_CR45 publication-title: Landslides doi: 10.1007/s10346-021-01693-7 – year: 2012 ident: 6560_CR31 publication-title: Math. Probl. Eng. doi: 10.1155/2012/974638 – volume: 35 start-page: 341 year: 2020 ident: 6560_CR60 publication-title: Geocarto Int. doi: 10.1080/10106049.2018.1516248 – year: 2019 ident: 6560_CR57 publication-title: Appl. Sci. doi: 10.3390/app9050942 – volume: 12 start-page: 1 year: 2020 ident: 6560_CR80 publication-title: Remote Sens. doi: 10.3390/rs12111737 – volume: 2020 start-page: 8830661 year: 2020 ident: 6560_CR61 publication-title: Geofluids doi: 10.1155/2020/8830661 – volume: 145 start-page: 164 year: 2016 ident: 6560_CR79 publication-title: CATENA doi: 10.1016/j.catena.2016.06.004 – volume: 36 start-page: 325 year: 1998 ident: 6560_CR54 publication-title: Environ. Geol. doi: 10.1007/s002540050348 – start-page: 573 volume-title: Landslides: Disaster Risk Reduction year: 2009 ident: 6560_CR4 doi: 10.1007/978-3-540-69970-5_30 – volume: 9 start-page: 49 year: 2018 ident: 6560_CR30 publication-title: Geom. Nat. Hazards Risk. doi: 10.1080/19475705.2017.1407368 – ident: 6560_CR71 – year: 2021 ident: 6560_CR82 publication-title: Geocarto Int. doi: 10.1080/10106049.2021.1892210 – volume: 10 start-page: 623 year: 2010 ident: 6560_CR50 publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-10-623-2010 – volume: 18 start-page: 1691 year: 2021 ident: 6560_CR7 publication-title: Landslides doi: 10.1007/s10346-020-01580-7 – volume: 11 start-page: 425 year: 2013 ident: 6560_CR18 publication-title: Landslides doi: 10.1007/s10346-013-0391-7 – volume: 122 start-page: 25 year: 2010 ident: 6560_CR22 publication-title: Geomorphology doi: 10.1016/j.geomorph.2010.05.009 – volume: 35 start-page: 1125 year: 2009 ident: 6560_CR28 publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2008.08.007 – volume: 662 start-page: 332 year: 2019 ident: 6560_CR43 publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.01.221 – volume: 41 start-page: 647 year: 2014 ident: 6560_CR72 publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-013-0679-x – volume-title: Natural Disaster Hotspots: A Global Risk Analysis year: 2005 ident: 6560_CR2 doi: 10.1596/0-8213-5930-4 – ident: 6560_CR5 – volume: 8 start-page: 649 year: 2017 ident: 6560_CR58 publication-title: Geom. Nat. Hazards Risk. doi: 10.1080/19475705.2016.1255667 – volume: 64 start-page: 65 year: 2002 ident: 6560_CR23 publication-title: Eng. Geol. doi: 10.1016/S0013-7952(01)00093-X – volume: 8 start-page: e1249, 1–18 year: 2018 ident: 6560_CR38 publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1249 – volume: 94 start-page: 401 year: 2008 ident: 6560_CR52 publication-title: Geomorphology doi: 10.1016/j.geomorph.2006.10.036 – volume: 35 start-page: 321 year: 2021 ident: 6560_CR39 publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658816.2020.1808897 – volume: 46 year: 2020 ident: 6560_CR67 publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2020.101201 – year: 2020 ident: 6560_CR64 publication-title: Geocarto Int. doi: 10.1080/10106049.2020.1831623 – volume: 7 start-page: 82512 year: 2019 ident: 6560_CR37 publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2923640 – volume: 14 start-page: 241 year: 2020 ident: 6560_CR36 publication-title: Front. Comput. Sci. doi: 10.1007/s11704-019-8208-z – volume: 195 year: 2020 ident: 6560_CR40 publication-title: CATENA doi: 10.1016/j.catena.2020.104805 – volume: 16 start-page: 1619 year: 2011 ident: 6560_CR3 publication-title: Electron. J. Geotech. Eng. – ident: 6560_CR63 doi: 10.1145/2939672.2939785 – volume: 52 start-page: 11 year: 2011 ident: 6560_CR10 publication-title: Appl. Clay Sci. doi: 10.1016/j.clay.2011.01.015 – ident: 6560_CR66 – volume: 31 start-page: 181 year: 1999 ident: 6560_CR24 publication-title: Geomorphology doi: 10.1016/S0169-555X(99)00078-1 – volume-title: Applied Logistic Regression Analysis: Sage University Series on Quantitative Applications in the Social Sciences year: 2002 ident: 6560_CR73 doi: 10.4135/9781412983433 – volume: 17 start-page: 17 year: 2016 ident: 6560_CR51 publication-title: J. Korean Geoenviron. Soc. doi: 10.14481/jkges.2016.17.4.17 – volume: 37 start-page: 209 year: 2006 ident: 6560_CR21 publication-title: Nat. Hazards. doi: 10.1007/s11069-005-4669-5 – volume: 4 start-page: 327 year: 2007 ident: 6560_CR34 publication-title: Landslides doi: 10.1007/s10346-007-0088-x – volume: 38 start-page: 23 year: 2012 ident: 6560_CR77 publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2011.04.012 – volume: 14 start-page: 1091 year: 2017 ident: 6560_CR42 publication-title: Landslides doi: 10.1007/s10346-016-0769-4 – ident: 6560_CR70 doi: 10.1038/s42256-019-0138-9 – volume: 45 start-page: 5 year: 2001 ident: 6560_CR59 publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 75 start-page: 229 year: 2004 ident: 6560_CR17 publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2004.06.001 – volume: 2 start-page: 1 year: 2020 ident: 6560_CR65 publication-title: SN Appl. Sci. doi: 10.1007/s42452-020-3060-1 – volume: 50 start-page: 283 year: 2019 ident: 6560_CR48 publication-title: Adv. Nat. Technol. Hazards Res. doi: 10.1007/978-3-319-77377-3_13 – volume: 8 start-page: 545 year: 2019 ident: 6560_CR53 publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi8120545 – start-page: 151 volume-title: Laser Scanning Applications in Landslide Assessment year: 2017 ident: 6560_CR20 doi: 10.1007/978-3-319-55342-9_8 – volume: 8 start-page: 910 year: 2008 ident: 6560_CR55 publication-title: J. Appl. Sci. doi: 10.3923/jas.2008.910.921 – volume: 13 start-page: 4776 year: 2021 ident: 6560_CR35 publication-title: Remote Sens. doi: 10.3390/rs13234776 – ident: 6560_CR56 – volume: 192 start-page: 101 year: 2015 ident: 6560_CR11 publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2015.04.004 – volume: 247 start-page: 12 year: 1996 ident: 6560_CR1 publication-title: Spec. Rep. Natl. Res. Counc. Transp. Res. Board. – volume: 9 start-page: 114 year: 2020 ident: 6560_CR46 publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi9020114 – volume: 9 start-page: 4993 year: 2020 ident: 6560_CR14 publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi9090553 – volume: 32 start-page: 956 year: 2017 ident: 6560_CR13 publication-title: Geocarto Int. doi: 10.1080/10106049.2016.1170892 |
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| Title | Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost) |
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