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مقاله

Monthly rainfall Forecasting using genetic programming and support vector machine

Hashemi, Sajjad، Samadian Fard, Saeed، Sharafi, Milad

چکیده

Rainfall and runoff estimation play a fundamental and effective role in the management and proper operation of the watershed, dams and reservoirs management, minimizing the damage caused by floods and droughts, and water resources management. The optimal performance of intelligent models has increased their use to predict various hydrological phenomena. Therefore, in this study, two intelligent models including, genetic programming and support vector machine were used to forecast the monthly precipitation of Ardabil province. For this purpose, precipitation, temperature, and relative humidity on a monthly scale were considered as the input parameters of the models. The results showed that the performances of both models were good and almost the same (mean absolute error of 0.8 and 0.721, respectively), but according to the evaluations, the support vector regression model had a relatively better performance (correlation coefficient 0.999) compared to another model. In general, it can be concluded that the support vector regression model has been more suitable for modeling and forecasting monthly precipitation in Ardabil province.

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