Precipitation forecasting by using wavelet-support vector machine conjunction model

Authors:
Ozgur Kisi;Mesut Cimen
Affiliations:
Erciyes University, Engineering Faculty, Civil Engineering Deptartment, 38039 Kayseri, Turkiye;Suleyman Demirel University, Engineering Faculty, Civil Engineering Deptartment, Isparta, Turkiye
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

A new wavelet-support vector machine conjunction model for daily precipitation forecast is proposed in this study. The conjunction method combining two methods, discrete wavelet transform and support vector machine, is compared with the single support vector machine for one-day-ahead precipitation forecasting. Daily precipitation data from Izmir and Afyon stations in Turkey are used in the study. The root mean square errors (RMSE), mean absolute errors (MAE), and correlation coefficient (R) statistics are used for the comparing criteria. The comparison results indicate that the conjunction method could increase the forecast accuracy and perform better than the single support vector machine. For the Izmir and Afyon stations, it is found that the conjunction models with RMSE=46.5mm, MAE=13.6mm, R=0.782 and RMSE=21.4mm, MAE=9.0mm, R=0.815 in test period is superior in forecasting daily precipitations than the best accurate support vector regression models with RMSE=71.6mm, MAE=19.6mm, R=0.276 and RMSE=38.7mm, MAE=14.2mm, R=0.103, respectively. The ANN method was also employed for the same data set and found that there is a slight difference between ANN and SVR methods.