University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 51 2 2020 04 20 Introducing a Nonlinear Model Based on Hybrid Machine Learning for Modeling and Prediction of Precipitation and Comparison with SDSM Method (Cases Studies: Shahrekord, Barez, and Yasuj) Introducing a Nonlinear Model Based on Hybrid Machine Learning for Modeling and Prediction of Precipitation and Comparison with SDSM Method (Cases Studies: Shahrekord, Barez, and Yasuj) 325 339 72682 10.22059/ijswr.2019.285141.668258 FA Mahdi Valikhan Anaraki Graduated MSc., Department of Water Resources Engineering and Management, Faculty of Civil Engineering, Semnan University, Semnan, Iran. 0000-0002-4820-5393 Sayed-Farhad Mousavi Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University Saeed Farzin Assistant Professor, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan Univercity, Semnan, Iran. Hojat Karami Assistant Professor, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan Univercity, Semnan, Iran. 0000-0002-2017-7204 Journal Article 2019 07 17 In the present study, a nonlinear hybrid model, based on multivariate adaptive regression splines (MARS), artificial neural networks (ANN) and K-nearest neighbor (KNN) has been presented for downscaling the precipitation of Shahrekord, Barez, and Yasuj under climate change conditions. This model, similar to SDSM, is composed of two steps; classification and regression. The MARS model is employed for classification of precipitation occurrence and the ANN and KNN are employed for determination of the amount of precipitation. The results of MARS showed that the mentioned model is more accurate than the SDSM model. Comparing the results of downscaled precipitation showed that the ANN model is more accurate than the SDSM and KNN in prediction of average annual and monthly precipitation. So that the R value for ANN was 54% more than the one in SDSM model, in Shahrekord. Also, according to the highest accuracy, standard deviation and skewness coefficient, the ANN, KNN and SDSM model ranked first, second, and third, respectively, for prediction of monthly average precipitation in three investigated stations. Eventually, the precipitation changes in the near future (2020-2040) and far future (2070-2100) periods were investigated under the A2 and B2 scenarios of the HADCM3 model. Results revealed that the lowest precipitation reduction is corresponded to ANN (in Shahrekord) and A2 scenario in the near future period and the highest precipitation reduction is corresponded to SDSM (in Yasuj) and A2 scenario in the far future period. Finally, it can be concluded that the proposed model is more accurate than the SDSM model and can be used as an alternative to the SDSM model. In the present study, a nonlinear hybrid model, based on multivariate adaptive regression splines (MARS), artificial neural networks (ANN) and K-nearest neighbor (KNN) has been presented for downscaling the precipitation of Shahrekord, Barez, and Yasuj under climate change conditions. This model, similar to SDSM, is composed of two steps; classification and regression. The MARS model is employed for classification of precipitation occurrence and the ANN and KNN are employed for determination of the amount of precipitation. The results of MARS showed that the mentioned model is more accurate than the SDSM model. Comparing the results of downscaled precipitation showed that the ANN model is more accurate than the SDSM and KNN in prediction of average annual and monthly precipitation. So that the R value for ANN was 54% more than the one in SDSM model, in Shahrekord. Also, according to the highest accuracy, standard deviation and skewness coefficient, the ANN, KNN and SDSM model ranked first, second, and third, respectively, for prediction of monthly average precipitation in three investigated stations. Eventually, the precipitation changes in the near future (2020-2040) and far future (2070-2100) periods were investigated under the A2 and B2 scenarios of the HADCM3 model. Results revealed that the lowest precipitation reduction is corresponded to ANN (in Shahrekord) and A2 scenario in the near future period and the highest precipitation reduction is corresponded to SDSM (in Yasuj) and A2 scenario in the far future period. Finally, it can be concluded that the proposed model is more accurate than the SDSM model and can be used as an alternative to the SDSM model. https://ijswr.ut.ac.ir/article_72682_f949ce0bf1539ada7cb95c35912aef26.pdf