Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco

Journal of Water and Land Development

article

My Hachem Bekri¹  Abdellah El Hmaidi¹  Habiba Ousmana¹  El Mati El Faleh¹  Mohamed Berrada¹  Kamal El Aissaoui¹  Ali Essahlaoui¹  Abdelhadi El Ouali¹ 

[1] Moulay Ismail University, Faculty of Sciences

关键词: Ansgemir watershed;    drought;    forecast;    modelling;    standardized precipitation index (SPI);    standardized precipitation-evapotranspiration index (SPEI);    support vectors regression (SVR);   

DOI  :  10.24425/jwld.2021.138174

学科分类：农业科学（综合）

来源: Instytut Technologiczno-Przyrodniczego / Institute of Technology and Life Sciences

【 摘 要 】

The purpose of this study is to develop mathematical models based on artificial intelligence: Models based on the support vectors regression (SVR) for drought forecast in the Ansegmir watershed (Upper Moulouya, Morocco). This study focuses on the prediction of the temporal aspect of the two drought indices (standardized precipitation index – SPI and standardized precipitation-evapotranspiration index – SPEI ) using six hydro-climatic variables relating to the period 1979–2013. The model SVR3-SPI: RBF, ε = 0.004, C = 20 and γ = 1.7 for the index SPI, and the model SVR3-SPEI: RBF ε = 0.004, C = 40 and γ = 0.167 for the SPEI index are significantly better in comparison to other models SVR1, SVR2 and SVR4. The SVR model for the SPI index gave a correlation coefficient of R = 0.92, MSE = 0.17 and MAE = 0.329 for the learning phase and R = 0.90, MSE = 0.18 and MAE = 0.313 for the testing phase. As for the SPEI index, the overlay is slightly poorer only in the case of the SPI index between the observed values and the predicted ones by the SVR model. It shows a very small gap between the observed and predicted values. The correlation coefficients R = 0.88 for the learning, R = 0.86 for testing remain higher and corresponding to a quadratic error average MSE = 0.21 and MAE = 0.351 for the learning and MSE = 0.21 and MAE = 0.350 for the testing phase. The prediction of drought by SVR model remain useful and would be extremely important for drought risk management.

【 授权许可】

Unknown