Enhancing robustness of monthly streamflow forecasting model using embedded-feature selection algorithm based on improved gray wolf optimizer

•Model hyperparameters and input variables (features) affect the forecasting results.•An embedded feature selection technique and their SVM-IGWO realization (EFS-SVMIGWO) is developed for streamflow prediction.•The proposed improved Grey Wolf Optimizer (IGWO) displays superior performance on feature...

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Vydáno v:	Journal of hydrology (Amsterdam) Ročník 617; s. 128995
Hlavní autoři:	Wang, Qingjie, Yue, Chunfang, Li, Xiaoqing, Liao, Pan, Li, Xiaoyao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.02.2023
Témata:	China Feature selection Hydrologic time series Improved gray wolf algorithm Passeriformes prediction runoff stream flow Streamflow prediction Support vector machine support vector machines time series analysis China Streamflow prediction Feature selection Support vector machine Hydrologic time series Improved gray wolf algorithm
ISSN:	0022-1694, 1879-2707
On-line přístup:	Získat plný text
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Abstract	•Model hyperparameters and input variables (features) affect the forecasting results.•An embedded feature selection technique and their SVM-IGWO realization (EFS-SVMIGWO) is developed for streamflow prediction.•The proposed improved Grey Wolf Optimizer (IGWO) displays superior performance on feature recognition and model parameter parallel optimization than GWO algorithms.•The test results showed that the EFS-SVMIGWO model is very competitive and superior to the benchmarked models. Accurate streamflow prediction plays an essential role in guaranteeing the sustainable utilization and management of water resources. In recent years, Artificial Intelligence (AI) models have been widely used for flow prediction. The performance of these models depends on the appropriate calibration of the input features and model parameters. Theoretically, the embedded feature selection method directly takes the final prediction model as a prediction indicator, which has the unique advantage of parallel optimization of feature and prediction model parameters compared with other methods. Despite being widely used in many other fields, its streamflow forecasting abilities are thus far unknown. In this paper, an embedded prediction model (EFS-SVMIGWO) with improved gray wolf optimizer (IGWO) and support vector machine (SVM) is proposed based on the principle of embedded feature selection method and validated with monthly runoff prediction at Kizil reservoir station in Xinjiang, China. The validation results demonstrate that the EFS-SVMIGWO model has consistently better accuracy and stable values than the benchmark methods (Including autoregressive integrated moving average, random forest, neural network and SVM models based on filtered selection methods). Moreover, IGWO is compared to differential evolution (DE), particle swarm optimization (PSO), whale optimization algorithm (WOA), sparrow search algorithm (SSA), and gray wolf optimizer (GWO), and the results show that IGWO has better convergence speed and solution quality in feature and model parameter parallel optimization tasks. Overall research and analysis indicate that the EFS-SVMIGWO model can exhibit convincing performance in monthly streamflow forecasting. Thus, it is of great importance to carefully choose the input variables and parameters to develop more effective models for forecasting monthly streamflow time series.
AbstractList	Accurate streamflow prediction plays an essential role in guaranteeing the sustainable utilization and management of water resources. In recent years, Artificial Intelligence (AI) models have been widely used for flow prediction. The performance of these models depends on the appropriate calibration of the input features and model parameters. Theoretically, the embedded feature selection method directly takes the final prediction model as a prediction indicator, which has the unique advantage of parallel optimization of feature and prediction model parameters compared with other methods. Despite being widely used in many other fields, its streamflow forecasting abilities are thus far unknown. In this paper, an embedded prediction model (EFS-SVMIGWO) with improved gray wolf optimizer (IGWO) and support vector machine (SVM) is proposed based on the principle of embedded feature selection method and validated with monthly runoff prediction at Kizil reservoir station in Xinjiang, China. The validation results demonstrate that the EFS-SVMIGWO model has consistently better accuracy and stable values than the benchmark methods (Including autoregressive integrated moving average, random forest, neural network and SVM models based on filtered selection methods). Moreover, IGWO is compared to differential evolution (DE), particle swarm optimization (PSO), whale optimization algorithm (WOA), sparrow search algorithm (SSA), and gray wolf optimizer (GWO), and the results show that IGWO has better convergence speed and solution quality in feature and model parameter parallel optimization tasks. Overall research and analysis indicate that the EFS-SVMIGWO model can exhibit convincing performance in monthly streamflow forecasting. Thus, it is of great importance to carefully choose the input variables and parameters to develop more effective models for forecasting monthly streamflow time series. •Model hyperparameters and input variables (features) affect the forecasting results.•An embedded feature selection technique and their SVM-IGWO realization (EFS-SVMIGWO) is developed for streamflow prediction.•The proposed improved Grey Wolf Optimizer (IGWO) displays superior performance on feature recognition and model parameter parallel optimization than GWO algorithms.•The test results showed that the EFS-SVMIGWO model is very competitive and superior to the benchmarked models. Accurate streamflow prediction plays an essential role in guaranteeing the sustainable utilization and management of water resources. In recent years, Artificial Intelligence (AI) models have been widely used for flow prediction. The performance of these models depends on the appropriate calibration of the input features and model parameters. Theoretically, the embedded feature selection method directly takes the final prediction model as a prediction indicator, which has the unique advantage of parallel optimization of feature and prediction model parameters compared with other methods. Despite being widely used in many other fields, its streamflow forecasting abilities are thus far unknown. In this paper, an embedded prediction model (EFS-SVMIGWO) with improved gray wolf optimizer (IGWO) and support vector machine (SVM) is proposed based on the principle of embedded feature selection method and validated with monthly runoff prediction at Kizil reservoir station in Xinjiang, China. The validation results demonstrate that the EFS-SVMIGWO model has consistently better accuracy and stable values than the benchmark methods (Including autoregressive integrated moving average, random forest, neural network and SVM models based on filtered selection methods). Moreover, IGWO is compared to differential evolution (DE), particle swarm optimization (PSO), whale optimization algorithm (WOA), sparrow search algorithm (SSA), and gray wolf optimizer (GWO), and the results show that IGWO has better convergence speed and solution quality in feature and model parameter parallel optimization tasks. Overall research and analysis indicate that the EFS-SVMIGWO model can exhibit convincing performance in monthly streamflow forecasting. Thus, it is of great importance to carefully choose the input variables and parameters to develop more effective models for forecasting monthly streamflow time series.
ArticleNumber	128995
Author	Liao, Pan Wang, Qingjie Li, Xiaoqing Yue, Chunfang Li, Xiaoyao
Author_xml	– sequence: 1 givenname: Qingjie surname: Wang fullname: Wang, Qingjie organization: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, PR China – sequence: 2 givenname: Chunfang surname: Yue fullname: Yue, Chunfang email: estheryue2017@163.com organization: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, PR China – sequence: 3 givenname: Xiaoqing surname: Li fullname: Li, Xiaoqing organization: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, PR China – sequence: 4 givenname: Pan surname: Liao fullname: Liao, Pan organization: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, PR China – sequence: 5 givenname: Xiaoyao surname: Li fullname: Li, Xiaoyao organization: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, PR China
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Cites_doi	10.1016/j.scitotenv.2020.136991 10.1016/j.jhydrol.2019.124435 10.1016/j.jhydrol.2019.124371 10.1016/j.asoc.2018.07.040 10.1016/j.jhydrol.2019.124299 10.1007/s11269-021-02786-7 10.1007/s11269-015-0962-6 10.1016/j.enconman.2017.04.007 10.1016/j.jhydrol.2014.01.062 10.1016/j.swevo.2012.09.002 10.1007/s00366-020-01028-5 10.1016/j.knosys.2016.07.026 10.1109/ACCESS.2020.2998437 10.1002/2017WR020482 10.1016/j.jhydrol.2018.12.040 10.1007/s11269-021-02861-z 10.1007/s12652-019-01569-8 10.1016/j.ins.2019.05.038 10.1016/j.atmosres.2017.06.014 10.1016/j.jhydrol.2019.02.025 10.1007/s12665-018-7376-8 10.1080/02626667.2020.1755436 10.1016/j.knosys.2018.05.009 10.1016/j.jhydrol.2015.08.022 10.1007/s10586-019-02913-5 10.1016/j.renene.2017.03.064 10.3390/w11010088 10.1016/j.neucom.2019.05.099 10.1016/j.apenergy.2012.04.001 10.1016/j.jhydrol.2020.125769 10.1155/2020/8506365 10.1016/j.energy.2015.08.045 10.1016/j.jhydrol.2020.124897 10.1016/j.advengsoft.2013.12.007 10.1016/j.jhydrol.2013.11.054 10.1007/s11269-018-1970-0 10.1016/j.jhydrol.2016.11.025 10.1016/j.jhydrol.2016.09.035 10.1016/j.jhydrol.2014.06.050 10.1016/j.jhydrol.2020.125717 10.1007/s11269-014-0610-6 10.1016/j.jhydrol.2019.03.046 10.1109/ACCESS.2019.2906757 10.1016/j.asoc.2014.02.002 10.1109/ACCESS.2020.3000040 10.1016/j.envsoft.2006.06.008 10.1016/j.jhydrol.2018.11.020 10.1016/j.patcog.2017.01.016 10.1016/j.knosys.2021.107379 10.1016/j.jhydrol.2020.124901 10.1016/j.jhydrol.2020.124627 10.1016/j.scs.2020.102562 10.1016/j.jhydrol.2018.01.015 10.1016/j.jhydrol.2018.10.064 10.1016/j.enconman.2014.06.041 10.1016/j.advengsoft.2016.01.008 10.1093/biomet/63.1.117 10.1016/j.neucom.2015.06.083 10.1016/j.apenergy.2019.04.188 10.1109/ACCESS.2021.3073261 10.1007/s13042-018-00913-2 10.2166/nh.2017.111 10.1007/s11269-020-02483-x 10.1002/2015WR016959 10.1016/j.eswa.2020.113917 10.1109/ACCESS.2021.3049547 10.1016/j.asoc.2017.03.002 10.1007/s10489-016-0767-1 10.13031/2013.23153 10.1016/j.jhydrol.2020.125168
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Keywords	Streamflow prediction Feature selection Support vector machine Hydrologic time series Improved gray wolf algorithm
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References	Guo, Xu, Sun, Xie (b0105) 2021; 597 Snieder, Shakir, Khan (b0295) 2020; 583 Dawson, Abrahart, See (b0060) 2007; 22 Al-Tashi, Abdulkadir, Rais, Mirjalili, Alhussian, Ragab, Alqushaibi (b0050) 2020; 8 Shibata (b0290) 1976; 63 Ji, Zhou, Huang (b0130) 2014; 28 Mehdizadeh, Sales (b0175) 2018; 32 Mirjalili, Mirjalili, Lewis (b0200) 2014; 69 Niu, Feng (b0235) 2021; 64 Ministry of Water Resources P.R.C. (b0185) 2008 Pashaei, Aydin (b0240) 2017; 56 Yang, Asanjan, Welles, Gao, Sorooshian, Liu (b0340) 2017; 53 Büyükşahin, Ertekin (b0055) 2019; 361 Hadi, Tombul, Salih, Al-Ansari, Yaseen (b0110) 2020; 8 Aljarah, Mafarja, Heidari, Faris, Zhang, Mirjalili (b0035) 2018; 71 Zhao, Lv, Lv, Sang, Wei, Zhu (b0365) 2021; 601 He, Wen, Liu, Du (b0120) 2014; 509 Masmoudi, S., Elghazel, H., Taieb, D., Yazar, O., Kallel, A., 2020. A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection. SCIENCE OF THE TOTAL ENVIRONMENT 715. Niu, Feng, Feng, Min, Cheng, Zhou (b0230) 2019; 11 Faris, Mafarja, Heidari, Aljarah, Al-Zoubi, Mirjalili, Fujita (b0090) 2018; 154 Li, Zhang, Wallace, Campbell (b0145) 2020; 589 Sudheer, Anand, Panigrahi, Mathur (b0305) 2013; 101 Mirjalili, Lewis (b0190) 2013; 9 Prasad, Deo, Li, Maraseni (b0245) 2017; 197 Tan, Lei, Wang, Wang, Wen, Ji, Kang (b0310) 2018; 567 Zhou, Guo, Chang (b0370) 2019; 570 Nadimi-Shahraki, Taghian, Mirjalili (b0215) 2021; 166 Shekhawat, Sharma, Kumar, Nayyar, Qureshi (b0285) 2021; 9 Fang, Huang, Ren, Huang, Huang, Cheng, Li (b0080) 2019; 568 Zhang, Zhou, Li, Fu, Peng (b0360) 2017; 143 Molajou, Nourani, Afshar, Khosravi, Brysiewicz (b0205) 2021; 35 Ren, Fang, Qu, Zhang, Shi (b0260) 2020; 586 Nelson, Illingworth (b0220) 1991 Stojković, Kostić, Plavšić, Prohaska (b0300) 2017; 544 Meng, Huang, Huang, Fang, Wang, Leng, Wang, Liang (b0180) 2021; 35 Luo, Yuan, Zhu, Xu, Meng, Peng (b0165) 2019; 568 Alamiedy, Anbar, Alqattan, Alzubi (b0025) 2020; 11 He, Luo, Li, Zuo, Xie (b0115) 2020; 34 Rezaie-Balf, Kim, Fallah, Alaghmand (b0270) 2019; 572 Abbasi, Farokhnia, Bahreinimotlagh, Roozbahani (b0005) 2021; 597 Diop, Bodian, Djaman, Yaseen, Deo, El-Shafie, Brown (b0070) 2018; 77 Faris, Mirjalili, Aljarah (b0095) 2019; 10 Adnan, R.M., Mostafa, R.R., Kisi, O., Yaseen, Z.M., Shahid, S., Zounemat-Kermani, M., 2021. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowledge-based Systems 230. Karimi, Shiri, Kisi, Xu (b0135) 2018; 49 Aljarah, Faris, Mirjalili, Al-Madi, Sheta, Mafarja (b0040) 2019; 22 Deb, Kiem, Willgoose (b0065) 2019; 571 Feng, Niu, Tang, Jiang, Xu, Liu, Zhang (b0100) 2020; 583 Wang, Chau, Xu, Chen (b0330) 2015; 29 Adnan, Liang, Heddam, Zounemat-Kermani, Kisi, Li (b0020) 2020; 586 Wang, Hu (b0335) 2015; 93 Segera, D., Mbuthia, M., Nyete, A. 2020. An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection. Biomed Research International. 2020. Taradeh, Mafarja, Heidari, Faris, Aljarah, Mirjalili, Fujita (b0320) 2019; 497 Liu, Tian, Li (b0155) 2012; 98 Zarshenas, Suzuki (b0350) 2016; 110 Rezaie-Balf, Zahmatkesh, Kim (b0265) 2017; 31 Lahouar, Slama (b0140) 2017; 109 Yaseen, Jaafar, Deo, Kisi, Adamowski, Quilty, El-Shafie (b0345) 2016; 542 Zhu, Zhu, Hu, Zhang, Zuo (b0375) 2017; 66 Salcedo-Sanz, Pastor-Sanchez, Prieto, Blanco-Aguilera, Garcia-Herrera (b0275) 2014; 87 Al-Tashi, Kadir, Rais, Mirjalili, Alhussian (b0045) 2019; 7 Huang, Chang, Huang, Chen (b0125) 2014; 511 Tikhamarine, Souag-Gamane, Ahmed, Kisi, El-Shafie (b0325) 2020; 582 Faris, Aljarah, Mirjalili (b0085) 2016; 45 Liu, Zhou, Chen, Guo (b0160) 2014; 519 Quilty, Adamowski, Khalil, Rathinasamy (b0250) 2016; 52 Taormina, Chau (b0315) 2015; 529 Alizadeh, Shourian, Yaseen (b0030) 2020; 65 Emary, Zawba, Hassanien (b0075) 2016; 172 Mirjalili, Lewis (b0195) 2016; 95 Ni, Wang, Wu, Wang, Tao, Zhang, Liu (b0225) 2020; 586 Raghavendra, Deka (b0255) 2014; 19 Abd Elminaam, Nabil, Ibraheem, Houssein (b0010) 2021; 9 Liu, Chen (b0150) 2019; 249 Zhang, Liu, Wang, Chen, Li (b0355) 2021; 37 Moriasi, Arnold, Liew, Bingner, Harmel, Veith (b0210) 2007; 50 Zhang (10.1016/j.jhydrol.2022.128995_b0360) 2017; 143 Nadimi-Shahraki (10.1016/j.jhydrol.2022.128995_b0215) 2021; 166 Rezaie-Balf (10.1016/j.jhydrol.2022.128995_b0265) 2017; 31 Li (10.1016/j.jhydrol.2022.128995_b0145) 2020; 589 10.1016/j.jhydrol.2022.128995_b0015 Raghavendra (10.1016/j.jhydrol.2022.128995_b0255) 2014; 19 Zhang (10.1016/j.jhydrol.2022.128995_b0355) 2021; 37 10.1016/j.jhydrol.2022.128995_b0170 Snieder (10.1016/j.jhydrol.2022.128995_b0295) 2020; 583 Tan (10.1016/j.jhydrol.2022.128995_b0310) 2018; 567 Faris (10.1016/j.jhydrol.2022.128995_b0095) 2019; 10 Stojković (10.1016/j.jhydrol.2022.128995_b0300) 2017; 544 Zarshenas (10.1016/j.jhydrol.2022.128995_b0350) 2016; 110 Büyükşahin (10.1016/j.jhydrol.2022.128995_b0055) 2019; 361 Deb (10.1016/j.jhydrol.2022.128995_b0065) 2019; 571 Guo (10.1016/j.jhydrol.2022.128995_b0105) 2021; 597 Ji (10.1016/j.jhydrol.2022.128995_b0130) 2014; 28 Niu (10.1016/j.jhydrol.2022.128995_b0235) 2021; 64 Taormina (10.1016/j.jhydrol.2022.128995_b0315) 2015; 529 Yang (10.1016/j.jhydrol.2022.128995_b0340) 2017; 53 Al-Tashi (10.1016/j.jhydrol.2022.128995_b0050) 2020; 8 Moriasi (10.1016/j.jhydrol.2022.128995_b0210) 2007; 50 Wang (10.1016/j.jhydrol.2022.128995_b0335) 2015; 93 Quilty (10.1016/j.jhydrol.2022.128995_b0250) 2016; 52 Ren (10.1016/j.jhydrol.2022.128995_b0260) 2020; 586 Shibata (10.1016/j.jhydrol.2022.128995_b0290) 1976; 63 Emary (10.1016/j.jhydrol.2022.128995_b0075) 2016; 172 Taradeh (10.1016/j.jhydrol.2022.128995_b0320) 2019; 497 Faris (10.1016/j.jhydrol.2022.128995_b0085) 2016; 45 Aljarah (10.1016/j.jhydrol.2022.128995_b0035) 2018; 71 He (10.1016/j.jhydrol.2022.128995_b0115) 2020; 34 Zhou (10.1016/j.jhydrol.2022.128995_b0370) 2019; 570 Liu (10.1016/j.jhydrol.2022.128995_b0160) 2014; 519 Pashaei (10.1016/j.jhydrol.2022.128995_b0240) 2017; 56 Rezaie-Balf (10.1016/j.jhydrol.2022.128995_b0270) 2019; 572 Zhao (10.1016/j.jhydrol.2022.128995_b0365) 2021; 601 Molajou (10.1016/j.jhydrol.2022.128995_b0205) 2021; 35 Lahouar (10.1016/j.jhydrol.2022.128995_b0140) 2017; 109 Faris (10.1016/j.jhydrol.2022.128995_b0090) 2018; 154 Abbasi (10.1016/j.jhydrol.2022.128995_b0005) 2021; 597 Sudheer (10.1016/j.jhydrol.2022.128995_b0305) 2013; 101 Mirjalili (10.1016/j.jhydrol.2022.128995_b0200) 2014; 69 Ministry of Water Resources P.R.C. (10.1016/j.jhydrol.2022.128995_b0185) 2008 Wang (10.1016/j.jhydrol.2022.128995_b0330) 2015; 29 Alamiedy (10.1016/j.jhydrol.2022.128995_b0025) 2020; 11 Liu (10.1016/j.jhydrol.2022.128995_b0155) 2012; 98 Feng (10.1016/j.jhydrol.2022.128995_b0100) 2020; 583 Karimi (10.1016/j.jhydrol.2022.128995_b0135) 2018; 49 Alizadeh (10.1016/j.jhydrol.2022.128995_b0030) 2020; 65 Prasad (10.1016/j.jhydrol.2022.128995_b0245) 2017; 197 Huang (10.1016/j.jhydrol.2022.128995_b0125) 2014; 511 Mehdizadeh (10.1016/j.jhydrol.2022.128995_b0175) 2018; 32 Adnan (10.1016/j.jhydrol.2022.128995_b0020) 2020; 586 Tikhamarine (10.1016/j.jhydrol.2022.128995_b0325) 2020; 582 Niu (10.1016/j.jhydrol.2022.128995_b0230) 2019; 11 Diop (10.1016/j.jhydrol.2022.128995_b0070) 2018; 77 Nelson (10.1016/j.jhydrol.2022.128995_b0220) 1991 Fang (10.1016/j.jhydrol.2022.128995_b0080) 2019; 568 Dawson (10.1016/j.jhydrol.2022.128995_b0060) 2007; 22 Mirjalili (10.1016/j.jhydrol.2022.128995_b0195) 2016; 95 Shekhawat (10.1016/j.jhydrol.2022.128995_b0285) 2021; 9 Liu (10.1016/j.jhydrol.2022.128995_b0150) 2019; 249 Hadi (10.1016/j.jhydrol.2022.128995_b0110) 2020; 8 10.1016/j.jhydrol.2022.128995_b0280 Al-Tashi (10.1016/j.jhydrol.2022.128995_b0045) 2019; 7 Meng (10.1016/j.jhydrol.2022.128995_b0180) 2021; 35 Abd Elminaam (10.1016/j.jhydrol.2022.128995_b0010) 2021; 9 Luo (10.1016/j.jhydrol.2022.128995_b0165) 2019; 568 Mirjalili (10.1016/j.jhydrol.2022.128995_b0190) 2013; 9 Salcedo-Sanz (10.1016/j.jhydrol.2022.128995_b0275) 2014; 87 Zhu (10.1016/j.jhydrol.2022.128995_b0375) 2017; 66 Ni (10.1016/j.jhydrol.2022.128995_b0225) 2020; 586 Aljarah (10.1016/j.jhydrol.2022.128995_b0040) 2019; 22 Yaseen (10.1016/j.jhydrol.2022.128995_b0345) 2016; 542 He (10.1016/j.jhydrol.2022.128995_b0120) 2014; 509
References_xml	– volume: 497 start-page: 219 year: 2019 end-page: 239 ident: b0320 article-title: An evolutionary gravitational search-based feature selection publication-title: Information Sciences – volume: 582 year: 2020 ident: b0325 article-title: Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm publication-title: Journal of Hydrology – volume: 29 start-page: 2655 year: 2015 end-page: 2675 ident: b0330 article-title: Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition publication-title: Water Resour Manage – volume: 66 start-page: 364 year: 2017 end-page: 374 ident: b0375 article-title: Subspace clustering guided unsupervised feature selection publication-title: PATTERN RECOGNITION – volume: 154 start-page: 43 year: 2018 end-page: 67 ident: b0090 article-title: An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems publication-title: Knowledge-based Systems – volume: 361 start-page: 151 year: 2019 end-page: 163 ident: b0055 article-title: Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition publication-title: Neurocomputing – year: 2008 ident: b0185 article-title: Standard for hydrological information and hydrological forecasting Ministry of Water Resources (GB/T 22482–2008 2008) – volume: 529 start-page: 1617 year: 2015 end-page: 1632 ident: b0315 article-title: Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines publication-title: Journal of Hydrology – volume: 571 start-page: 749 year: 2019 end-page: 764 ident: b0065 article-title: Mechanisms influencing non-stationarity in rainfall-runoff relationships in southeast Australia publication-title: Journal of Hydrology – volume: 8 start-page: 101993 year: 2020 end-page: 102006 ident: b0110 article-title: The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow publication-title: IEEE Access – volume: 101 start-page: 18 year: 2013 end-page: 23 ident: b0305 article-title: Streamflow forecasting by SVM with quantum behaved particle swarm optimization publication-title: Neurocomputing – reference: Adnan, R.M., Mostafa, R.R., Kisi, O., Yaseen, Z.M., Shahid, S., Zounemat-Kermani, M., 2021. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowledge-based Systems 230. – volume: 583 year: 2020 ident: b0295 article-title: A comprehensive comparison of four input variable selection methods for artificial neural network flow forecasting models publication-title: Journal of Hydrology – volume: 77 year: 2018 ident: b0070 article-title: The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River publication-title: Environmental Earth Sciences – volume: 172 start-page: 371 year: 2016 end-page: 381 ident: b0075 article-title: Binary grey wolf optimization approaches for feature selection publication-title: Neurocomputing – volume: 50 start-page: 885 year: 2007 end-page: 900 ident: b0210 article-title: Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations publication-title: Transactions of the ASABE – volume: 31 start-page: 3843 year: 2017 end-page: 3865 ident: b0265 article-title: Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non-Parametric Paradigm vs publication-title: Model Classification Methods. Water Resour Manage – volume: 572 start-page: 470 year: 2019 end-page: 485 ident: b0270 article-title: Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea publication-title: Journal of Hydrology – volume: 52 start-page: 2299 year: 2016 end-page: 2326 ident: b0250 article-title: Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling publication-title: Water Resour. Res. – volume: 64 start-page: 1 year: 2021 end-page: 12 ident: b0235 article-title: Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management publication-title: Sustainable Cities and Society – year: 1991 ident: b0220 article-title: A Practical Guide to Neural Nets – volume: 542 start-page: 603 year: 2016 end-page: 614 ident: b0345 article-title: Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq publication-title: Journal of Hydrology – volume: 9 start-page: 1 year: 2013 end-page: 14 ident: b0190 article-title: S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization publication-title: Swarm and Evolutionary Computation – volume: 93 start-page: 41 year: 2015 end-page: 56 ident: b0335 article-title: A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model publication-title: Energy – volume: 65 start-page: 1374 year: 2020 end-page: 1384 ident: b0030 article-title: Simulating monthly streamflow using a hybrid feature selection approach integrated with an intelligence model publication-title: Hydrological Sciences Journal-Journal Des Sciences Hydrologiques – volume: 586 year: 2020 ident: b0020 article-title: Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs publication-title: Journal of Hydrology – volume: 35 start-page: 2385 year: 2021 ident: b0205 article-title: Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling publication-title: Water Resour Manage – volume: 586 year: 2020 ident: b0225 article-title: Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model publication-title: Journal of Hydrology – volume: 11 start-page: 3735 year: 2020 end-page: 3756 ident: b0025 article-title: Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm publication-title: Journal of Ambient Intelligence and Humanized Computing – volume: 7 start-page: 39496 year: 2019 end-page: 39508 ident: b0045 article-title: Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection publication-title: IEEE Access – volume: 143 start-page: 360 year: 2017 end-page: 376 ident: b0360 article-title: A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting publication-title: Energy Conversion and Management – volume: 32 start-page: 3001 year: 2018 end-page: 3022 ident: b0175 article-title: A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow publication-title: Water Resour Manage – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b0195 article-title: The Whale Optimization Algorithm publication-title: Advances in Engineering Software – reference: Masmoudi, S., Elghazel, H., Taieb, D., Yazar, O., Kallel, A., 2020. A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection. SCIENCE OF THE TOTAL ENVIRONMENT 715. – volume: 34 start-page: 865 year: 2020 end-page: 884 ident: b0115 article-title: A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting publication-title: Water Resour Manage – volume: 597 year: 2021 ident: b0105 article-title: Multi-step-ahead forecast of reservoir water availability with improved quantum-based GWO coupled with the AI-based LSSVM model publication-title: Journal of Hydrology – volume: 98 start-page: 415 year: 2012 end-page: 424 ident: b0155 article-title: Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction publication-title: Applied Energy – volume: 10 start-page: 2901 year: 2019 end-page: 2920 ident: b0095 article-title: Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme publication-title: International Journal of Machine Learning and Cybernetics – volume: 249 start-page: 392 year: 2019 end-page: 408 ident: b0150 article-title: Data processing strategies in wind energy forecasting models and applications: A comprehensive review publication-title: Applied Energy – volume: 56 start-page: 94 year: 2017 end-page: 106 ident: b0240 article-title: Binary black hole algorithm for feature selection and classification on biological data publication-title: Applied Soft Computing – volume: 589 year: 2020 ident: b0145 article-title: Estimating annual runoff in response to forest change: A statistical method based on random forest publication-title: Journal of Hydrology – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: b0200 article-title: Grey Wolf Optimizer publication-title: Advances in Engineering Software – volume: 9 start-page: 60136 year: 2021 end-page: 60153 ident: b0010 article-title: An Efficient Marine Predators Algorithm for Feature Selection publication-title: IEEE Access – reference: Segera, D., Mbuthia, M., Nyete, A. 2020. An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection. Biomed Research International. 2020. – volume: 53 start-page: 2786 year: 2017 end-page: 2812 ident: b0340 article-title: Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information publication-title: Water Resour. Res. – volume: 109 start-page: 529 year: 2017 end-page: 541 ident: b0140 article-title: Hour-ahead wind power forecast based on random forests publication-title: Renewable Energy – volume: 110 start-page: 191 year: 2016 end-page: 201 ident: b0350 article-title: Binary coordinate ascent: An efficient optimization technique for feature subset selection for machine learning publication-title: Knowledge-based Systems – volume: 71 start-page: 964 year: 2018 end-page: 979 ident: b0035 article-title: Asynchronous accelerating multi-leader salp chains for feature selection publication-title: Applied Soft Computing – volume: 22 start-page: 1317 year: 2019 end-page: 1345 ident: b0040 article-title: Evolving neural networks using bird swarm algorithm for data classification and regression applications publication-title: Cluster Computing – volume: 87 start-page: 10 year: 2014 end-page: 18 ident: b0275 article-title: Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization - Extreme learning machine approach publication-title: Energy Conversion and Management – volume: 583 year: 2020 ident: b0100 article-title: Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization publication-title: Journal of Hydrology – volume: 567 start-page: 767 year: 2018 end-page: 780 ident: b0310 article-title: An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach publication-title: Journal of Hydrology – volume: 544 start-page: 555 year: 2017 end-page: 566 ident: b0300 article-title: A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates publication-title: Journal of Hydrology – volume: 570 start-page: 343 year: 2019 end-page: 355 ident: b0370 article-title: Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts publication-title: Journal of Hydrology – volume: 63 start-page: 117 year: 1976 end-page: 126 ident: b0290 article-title: Selection of the order of an autoregressive model by Akaike's information criterion publication-title: Biometrika – volume: 568 start-page: 184 year: 2019 end-page: 193 ident: b0165 article-title: A hybrid support vector regression framework for streamflow forecast publication-title: Journal of Hydrology – volume: 601 year: 2021 ident: b0365 article-title: Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer publication-title: Journal of Hydrology – volume: 35 start-page: 1321 year: 2021 end-page: 1337 ident: b0180 article-title: A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework publication-title: Water Resour Manage – volume: 166 year: 2021 ident: b0215 article-title: An improved grey wolf optimizer for solving engineering problems publication-title: Expert Systems with Applications – volume: 22 start-page: 1034 year: 2007 end-page: 1052 ident: b0060 article-title: HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts publication-title: Environmental Modelling & Software – volume: 519 start-page: 2822 year: 2014 end-page: 2831 ident: b0160 article-title: Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting publication-title: Journal of Hydrology – volume: 586 year: 2020 ident: b0260 article-title: Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets publication-title: Journal of Hydrology – volume: 509 start-page: 379 year: 2014 end-page: 386 ident: b0120 article-title: A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region publication-title: Journal of Hydrology – volume: 49 start-page: 658 year: 2018 end-page: 669 ident: b0135 article-title: Forecasting daily streamflow values: assessing heuristic models publication-title: Hydrology Research – volume: 9 start-page: 14867 year: 2021 end-page: 14882 ident: b0285 article-title: bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection publication-title: IEEE Access – volume: 597 year: 2021 ident: b0005 article-title: A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction publication-title: Journal of Hydrology – volume: 8 start-page: 106247 year: 2020 end-page: 106263 ident: b0050 article-title: Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification publication-title: IEEE Access – volume: 19 start-page: 372 year: 2014 end-page: 386 ident: b0255 article-title: Support vector machine applications in the field of hydrology: A review publication-title: Applied Soft Computing – volume: 45 start-page: 322 year: 2016 end-page: 332 ident: b0085 article-title: Training feedforward neural networks using multi-verse optimizer for binary classification problems publication-title: Applied Intelligence – volume: 197 start-page: 42 year: 2017 end-page: 63 ident: b0245 article-title: Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm publication-title: Atmospheric Research – volume: 37 start-page: 3741 year: 2021 end-page: 3770 ident: b0355 article-title: Boosted binary Harris hawks optimizer and feature selection publication-title: Engineering with Computers – volume: 511 start-page: 764 year: 2014 end-page: 775 ident: b0125 article-title: Monthly streamflow prediction using modified EMD-based support vector machine publication-title: Journal of Hydrology – volume: 28 start-page: 2435 year: 2014 end-page: 2451 ident: b0130 article-title: Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression publication-title: Water Resour Manage – volume: 568 start-page: 534 year: 2019 end-page: 550 ident: b0080 article-title: Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models publication-title: Journal of Hydrology – volume: 11 year: 2019 ident: b0230 article-title: Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir publication-title: Water – year: 2008 ident: 10.1016/j.jhydrol.2022.128995_b0185 – ident: 10.1016/j.jhydrol.2022.128995_b0170 doi: 10.1016/j.scitotenv.2020.136991 – volume: 582 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0325 article-title: Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2019.124435 – volume: 586 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0020 article-title: Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2019.124371 – volume: 71 start-page: 964 year: 2018 ident: 10.1016/j.jhydrol.2022.128995_b0035 article-title: Asynchronous accelerating multi-leader salp chains for feature selection publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.07.040 – volume: 583 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0295 article-title: A comprehensive comparison of four input variable selection methods for artificial neural network flow forecasting models publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2019.124299 – volume: 35 start-page: 1321 issue: 04 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0180 article-title: A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework publication-title: Water Resour Manage doi: 10.1007/s11269-021-02786-7 – volume: 29 start-page: 2655 issue: 08 year: 2015 ident: 10.1016/j.jhydrol.2022.128995_b0330 article-title: Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition publication-title: Water Resour Manage doi: 10.1007/s11269-015-0962-6 – volume: 143 start-page: 360 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0360 article-title: A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2017.04.007 – volume: 511 start-page: 764 year: 2014 ident: 10.1016/j.jhydrol.2022.128995_b0125 article-title: Monthly streamflow prediction using modified EMD-based support vector machine publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2014.01.062 – volume: 9 start-page: 1 year: 2013 ident: 10.1016/j.jhydrol.2022.128995_b0190 article-title: S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2012.09.002 – volume: 37 start-page: 3741 issue: 04 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0355 article-title: Boosted binary Harris hawks optimizer and feature selection publication-title: Engineering with Computers doi: 10.1007/s00366-020-01028-5 – volume: 110 start-page: 191 year: 2016 ident: 10.1016/j.jhydrol.2022.128995_b0350 article-title: Binary coordinate ascent: An efficient optimization technique for feature subset selection for machine learning publication-title: Knowledge-based Systems doi: 10.1016/j.knosys.2016.07.026 – volume: 8 start-page: 101993 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0110 article-title: The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2998437 – volume: 53 start-page: 2786 issue: 04 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0340 article-title: Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information publication-title: Water Resour. Res. doi: 10.1002/2017WR020482 – volume: 570 start-page: 343 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0370 article-title: Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2018.12.040 – volume: 35 start-page: 2385 issue: 08 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0205 article-title: Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling publication-title: Water Resour Manage doi: 10.1007/s11269-021-02861-z – volume: 11 start-page: 3735 issue: 09 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0025 article-title: Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm publication-title: Journal of Ambient Intelligence and Humanized Computing doi: 10.1007/s12652-019-01569-8 – volume: 497 start-page: 219 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0320 article-title: An evolutionary gravitational search-based feature selection publication-title: Information Sciences doi: 10.1016/j.ins.2019.05.038 – volume: 197 start-page: 42 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0245 article-title: Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm publication-title: Atmospheric Research doi: 10.1016/j.atmosres.2017.06.014 – volume: 571 start-page: 749 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0065 article-title: Mechanisms influencing non-stationarity in rainfall-runoff relationships in southeast Australia publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2019.02.025 – volume: 77 issue: 5 year: 2018 ident: 10.1016/j.jhydrol.2022.128995_b0070 article-title: The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River publication-title: Environmental Earth Sciences doi: 10.1007/s12665-018-7376-8 – volume: 65 start-page: 1374 issue: 08 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0030 article-title: Simulating monthly streamflow using a hybrid feature selection approach integrated with an intelligence model publication-title: Hydrological Sciences Journal-Journal Des Sciences Hydrologiques doi: 10.1080/02626667.2020.1755436 – volume: 154 start-page: 43 year: 2018 ident: 10.1016/j.jhydrol.2022.128995_b0090 article-title: An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems publication-title: Knowledge-based Systems doi: 10.1016/j.knosys.2018.05.009 – volume: 529 start-page: 1617 year: 2015 ident: 10.1016/j.jhydrol.2022.128995_b0315 article-title: Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2015.08.022 – volume: 22 start-page: 1317 issue: 04 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0040 article-title: Evolving neural networks using bird swarm algorithm for data classification and regression applications publication-title: Cluster Computing doi: 10.1007/s10586-019-02913-5 – volume: 109 start-page: 529 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0140 article-title: Hour-ahead wind power forecast based on random forests publication-title: Renewable Energy doi: 10.1016/j.renene.2017.03.064 – volume: 11 issue: 1 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0230 article-title: Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir publication-title: Water doi: 10.3390/w11010088 – volume: 361 start-page: 151 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0055 article-title: Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.05.099 – volume: 98 start-page: 415 year: 2012 ident: 10.1016/j.jhydrol.2022.128995_b0155 article-title: Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction publication-title: Applied Energy doi: 10.1016/j.apenergy.2012.04.001 – volume: 597 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0105 article-title: Multi-step-ahead forecast of reservoir water availability with improved quantum-based GWO coupled with the AI-based LSSVM model publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2020.125769 – ident: 10.1016/j.jhydrol.2022.128995_b0280 doi: 10.1155/2020/8506365 – volume: 93 start-page: 41 year: 2015 ident: 10.1016/j.jhydrol.2022.128995_b0335 publication-title: Energy doi: 10.1016/j.energy.2015.08.045 – volume: 586 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0260 article-title: Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2020.124897 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.jhydrol.2022.128995_b0200 article-title: Grey Wolf Optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 509 start-page: 379 year: 2014 ident: 10.1016/j.jhydrol.2022.128995_b0120 article-title: A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2013.11.054 – volume: 32 start-page: 3001 issue: 09 year: 2018 ident: 10.1016/j.jhydrol.2022.128995_b0175 article-title: A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow publication-title: Water Resour Manage doi: 10.1007/s11269-018-1970-0 – volume: 544 start-page: 555 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0300 article-title: A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2016.11.025 – volume: 542 start-page: 603 year: 2016 ident: 10.1016/j.jhydrol.2022.128995_b0345 article-title: Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2016.09.035 – volume: 519 start-page: 2822 year: 2014 ident: 10.1016/j.jhydrol.2022.128995_b0160 article-title: Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2014.06.050 – volume: 31 start-page: 3843 issue: 12 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0265 article-title: Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non-Parametric Paradigm vs publication-title: Model Classification Methods. Water Resour Manage – volume: 597 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0005 article-title: A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2020.125717 – volume: 28 start-page: 2435 issue: 09 year: 2014 ident: 10.1016/j.jhydrol.2022.128995_b0130 article-title: Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression publication-title: Water Resour Manage doi: 10.1007/s11269-014-0610-6 – volume: 572 start-page: 470 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0270 article-title: Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2019.03.046 – volume: 7 start-page: 39496 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0045 article-title: Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2906757 – volume: 19 start-page: 372 year: 2014 ident: 10.1016/j.jhydrol.2022.128995_b0255 article-title: Support vector machine applications in the field of hydrology: A review publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2014.02.002 – volume: 8 start-page: 106247 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0050 article-title: Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3000040 – volume: 22 start-page: 1034 issue: 07 year: 2007 ident: 10.1016/j.jhydrol.2022.128995_b0060 article-title: HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts publication-title: Environmental Modelling & Software doi: 10.1016/j.envsoft.2006.06.008 – volume: 568 start-page: 534 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0080 article-title: Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2018.11.020 – volume: 66 start-page: 364 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0375 article-title: Subspace clustering guided unsupervised feature selection publication-title: PATTERN RECOGNITION doi: 10.1016/j.patcog.2017.01.016 – ident: 10.1016/j.jhydrol.2022.128995_b0015 doi: 10.1016/j.knosys.2021.107379 – volume: 586 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0225 article-title: Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2020.124901 – volume: 583 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0100 article-title: Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2020.124627 – volume: 64 start-page: 1 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0235 article-title: Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management publication-title: Sustainable Cities and Society doi: 10.1016/j.scs.2020.102562 – volume: 567 start-page: 767 year: 2018 ident: 10.1016/j.jhydrol.2022.128995_b0310 article-title: An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2018.01.015 – volume: 568 start-page: 184 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0165 article-title: A hybrid support vector regression framework for streamflow forecast publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2018.10.064 – volume: 87 start-page: 10 year: 2014 ident: 10.1016/j.jhydrol.2022.128995_b0275 article-title: Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization - Extreme learning machine approach publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2014.06.041 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.jhydrol.2022.128995_b0195 article-title: The Whale Optimization Algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 63 start-page: 117 issue: 1 year: 1976 ident: 10.1016/j.jhydrol.2022.128995_b0290 article-title: Selection of the order of an autoregressive model by Akaike's information criterion publication-title: Biometrika doi: 10.1093/biomet/63.1.117 – volume: 172 start-page: 371 year: 2016 ident: 10.1016/j.jhydrol.2022.128995_b0075 article-title: Binary grey wolf optimization approaches for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.06.083 – volume: 249 start-page: 392 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0150 article-title: Data processing strategies in wind energy forecasting models and applications: A comprehensive review publication-title: Applied Energy doi: 10.1016/j.apenergy.2019.04.188 – volume: 9 start-page: 60136 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0010 article-title: An Efficient Marine Predators Algorithm for Feature Selection publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3073261 – volume: 10 start-page: 2901 issue: 10 year: 2019 ident: 10.1016/j.jhydrol.2022.128995_b0095 article-title: Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme publication-title: International Journal of Machine Learning and Cybernetics doi: 10.1007/s13042-018-00913-2 – volume: 49 start-page: 658 issue: 03 year: 2018 ident: 10.1016/j.jhydrol.2022.128995_b0135 article-title: Forecasting daily streamflow values: assessing heuristic models publication-title: Hydrology Research doi: 10.2166/nh.2017.111 – volume: 34 start-page: 865 issue: 02 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0115 article-title: A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting publication-title: Water Resour Manage doi: 10.1007/s11269-020-02483-x – volume: 52 start-page: 2299 issue: 03 year: 2016 ident: 10.1016/j.jhydrol.2022.128995_b0250 article-title: Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling publication-title: Water Resour. Res. doi: 10.1002/2015WR016959 – volume: 166 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0215 article-title: An improved grey wolf optimizer for solving engineering problems publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113917 – volume: 9 start-page: 14867 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0285 article-title: bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3049547 – volume: 56 start-page: 94 year: 2017 ident: 10.1016/j.jhydrol.2022.128995_b0240 article-title: Binary black hole algorithm for feature selection and classification on biological data publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2017.03.002 – volume: 101 start-page: 18 issue: 04 year: 2013 ident: 10.1016/j.jhydrol.2022.128995_b0305 article-title: Streamflow forecasting by SVM with quantum behaved particle swarm optimization publication-title: Neurocomputing – volume: 601 issue: 06 year: 2021 ident: 10.1016/j.jhydrol.2022.128995_b0365 article-title: Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer publication-title: Journal of Hydrology – volume: 45 start-page: 322 issue: 02 year: 2016 ident: 10.1016/j.jhydrol.2022.128995_b0085 article-title: Training feedforward neural networks using multi-verse optimizer for binary classification problems publication-title: Applied Intelligence doi: 10.1007/s10489-016-0767-1 – volume: 50 start-page: 885 issue: 03 year: 2007 ident: 10.1016/j.jhydrol.2022.128995_b0210 article-title: Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations publication-title: Transactions of the ASABE doi: 10.13031/2013.23153 – volume: 589 year: 2020 ident: 10.1016/j.jhydrol.2022.128995_b0145 article-title: Estimating annual runoff in response to forest change: A statistical method based on random forest publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2020.125168 – year: 1991 ident: 10.1016/j.jhydrol.2022.128995_b0220
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Snippet	•Model hyperparameters and input variables (features) affect the forecasting results.•An embedded feature selection technique and their SVM-IGWO realization... Accurate streamflow prediction plays an essential role in guaranteeing the sustainable utilization and management of water resources. In recent years,...
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SubjectTerms	China Feature selection Hydrologic time series Improved gray wolf algorithm Passeriformes prediction runoff stream flow Streamflow prediction Support vector machine support vector machines time series analysis
Title	Enhancing robustness of monthly streamflow forecasting model using embedded-feature selection algorithm based on improved gray wolf optimizer
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