A Unified Model for Estimation of Reference Evapotranspiration Using an Assembly of Ensemble Learners Coupled with Swarm Intelligence Optimizers
Several machine learning models and their ensembles have been suggested for reference evapotranspiration (ET0) modeling at different climatic regions. Researchers reported that optimizing model hyperparameters using an intelligent algorithm significantly improves the performance of such models. Howe...
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| Veröffentlicht in: | International Research Journal of Multidisciplinary Technovation S. 1 - 26 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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30.11.2025
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| ISSN: | 2582-1040, 2582-1040 |
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| Abstract | Several machine learning models and their ensembles have been suggested for reference evapotranspiration (ET0) modeling at different climatic regions. Researchers reported that optimizing model hyperparameters using an intelligent algorithm significantly improves the performance of such models. However, ensemble models hybridized with hyperparameter optimizers have hardly been applied for the precise estimation of ET0 worldwide. The current research is devoted to designing sixteen hybrid versions of four ensemble models, alternatively coupled with four popular swarm intelligence optimization algorithms and finding the best-fit model against different input combinations of available climatic parameters for the groundwater-stressed region of North Bengal, India. The performances of four ensemble models and their sixteen hybrid versions were compared in terms of four well-recognized statistical metrics: the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). Experimental results depicted that in nearly 92% of cases, the hybrid versions outperformed the primary ensemble models, irrespective of the available climatic parameters. In most cases, the ensemble models hybridized with the whale optimization algorithm (WOA) produced the highest estimation accuracy, followed by the sailfish optimizer (SFO). Solar radiation was also found to be the most significant climatic parameter for estimating ET0 in this region. |
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| AbstractList | Several machine learning models and their ensembles have been suggested for reference evapotranspiration (ET0) modeling at different climatic regions. Researchers reported that optimizing model hyperparameters using an intelligent algorithm significantly improves the performance of such models. However, ensemble models hybridized with hyperparameter optimizers have hardly been applied for the precise estimation of ET0 worldwide. The current research is devoted to designing sixteen hybrid versions of four ensemble models, alternatively coupled with four popular swarm intelligence optimization algorithms and finding the best-fit model against different input combinations of available climatic parameters for the groundwater-stressed region of North Bengal, India. The performances of four ensemble models and their sixteen hybrid versions were compared in terms of four well-recognized statistical metrics: the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). Experimental results depicted that in nearly 92% of cases, the hybrid versions outperformed the primary ensemble models, irrespective of the available climatic parameters. In most cases, the ensemble models hybridized with the whale optimization algorithm (WOA) produced the highest estimation accuracy, followed by the sailfish optimizer (SFO). Solar radiation was also found to be the most significant climatic parameter for estimating ET0 in this region. |
| Author | Sarkar, Uditendu Banerjee, Gouravmoy Ghosh, Indrajit |
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| Cites_doi | 10.1016/j.agwat.2018.06.018 10.1016/j.compag.2017.01.027 10.13031/2013.7049 10.1155/2013/281523 10.1088/1742-6596/2224/1/012006 10.1109/ACCESS.2020.2971354 10.1016/j.jhydrol.2019.123958 10.1029/2020WR027562 10.54386/jam.v26i1.2462 10.1145/3219819.3220058 10.1007/s13201-023-01895-5 10.1016/j.agwat.2017.08.003 10.1016/j.inpa.2020.02.003 10.3390/agronomy14050939 10.1016/j.advengsoft.2013.12.007 10.1016/j.jhydrol.2016.11.059 10.1016/j.agrformet.2018.08.019 10.1007/s11600-020-00509-x 10.1109/4235.585893 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 10.1016/j.advengsoft.2016.01.008 10.1214/aos/1013203451 10.1007/s00704-019-03007-3 10.1007/s00521-020-04800-2 10.1080/20964471.2024.2423431 10.1016/j.scitotenv.2019.135653 10.1016/j.agwat.2024.108779 10.1080/19942060.2019.1645045 10.1061/(ASCE)HE.1943-5584.0000366 10.2166/nh.2019.060 10.1007/978-1-4842-3564-5_12 10.1061/(ASCE)0733-9437(2003)129:5(336) 10.1016/j.rser.2022.112364 10.3390/agronomy10010101 10.3934/geosci.2021016 10.1016/j.jhydrol.2019.03.028 10.1371/journal.pone.0235324 10.1016/j.advengsoft.2017.07.002 10.1016/j.jhydrol.2020.125087 10.1016/j.compag.2020.105358 10.54386/jam.v18i2.958 10.1145/2939672.2939785 10.1016/j.compag.2019.104937 10.1007/s00521-021-06421-9 10.1134/S2079096120040150 10.15244/pjoes/136348 10.36253/ijam-1373 10.32604/cmes.2020.011004 10.1016/j.engappai.2019.01.001 10.1155/2019/9575782 10.1016/j.agrformet.2014.10.008 10.1007/s11356-024-33987-3 10.1016/j.scs.2020.102275 10.1002/widm.1301 10.1007/s00704-019-02852-6 10.1061/(ASCE)IR.1943-4774.0000664 10.1109/ACCESS.2020.2999540 10.1080/02626667.2019.1601727 10.1016/j.compag.2015.04.012 10.1007/s00704-023-04760-2 10.1016/j.jhydrol.2021.126538 10.1117/1.JRS.14.038504 10.1002/joc.5064 10.3390/w12030643 10.1016/j.compag.2010.01.001 10.1016/j.asoc.2021.107478 10.1109/ACCESS.2020.2987689 10.1088/1755-1315/861/7/072039 10.3390/w14132027 10.1016/j.agwat.2020.106594 10.13031/2013.26773 10.1007/s41101-020-00087-5 10.54386/jam.v22i2.158 10.1007/s12517-020-06293-8 10.1007/s13201-024-02308-x 10.1007/s12559-020-09730-8 10.1016/j.ecolind.2024.112203 10.1002/pa.2031 10.1038/s41598-022-04923-7 |
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