Prediction of daily precipitation using wavelet-neural networks

Partal T., Cığızoğlu H. K.

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, vol.54, no.2, pp.234-246, 2009 (SCI-Expanded) identifier identifier


This study aims to predict the daily precipitation from meteorological data from Turkey using the wavelet-neural network method, which combines two methods: discrete wavelet transform (DWT) and artificial neural networks (ANN). The wavelet-ANN model provides a good fit with the observed data, in particular for zero precipitation in the summer months, and for the peaks in the testing period. The results indicate that wavelet-ANN model estimations are significantly superior to those obtained by either a conventional ANN model or a multi linear regression model. In particular, the improvement provided by the new approach in estimating the peak values had a noticeably high positive effect on the performance evaluation criteria. Inclusion of the summed sub-series in the ANN input layer brings a new perspective to the discussions related to the physics involved in the ANN structure.