Precipitation forecasting by using wavelet-support vector machine conjunction model

• Published in: Engineering applications of artificial intelligence Vol. 25; no. 4; pp. 783 - 792
• Main Authors: Kisi, Ozgur, Cimen, Mesut
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-06-2012
• Subjects: Discrete wavelet transform; Forecast; Forecasting; Learning theory; Mathematical analysis; Mathematical models; Neural networks; Precipitation; Support vector machine; Support vector machines; Vectors (mathematics); Precipitation; Discrete wavelet transform; Support vector machine; Forecast
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2011.11.003

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.