Data mining based on wavelet and decision tree for rainfall-runoff simulation

This study introduced a new hybrid model (Wavelet-M5 model) which combines the wavelet transforms andM5model tree for rainfall-runoff modeling. For this purpose, themain time serieswere decomposed to several sub-signals by the wavelet transform, at first. Then, the obtained sub-time series were imposed as input data to M5 model tree, and finally, the related linear regressions were presented by M5 model tree. This new technique was applied on the monthly time series of Sardrud catchment and the results were also compared with other models like WANN and sole M5model tree. The results showed that the accuracy of the proposed model is better than the previous models and also indicated the effect of data pre-processing on the performance of M5 model tree. The determination coefficient of the training stage was 0.80 and improved 31% than the M5model tree for Sardrud catchment which is recognized as a normal watershed with a regular four seasons’ pattern. doi: 10.2166/nh.2018.049 s://iwaponline.com/hr/article-pdf/50/1/75/524319/nh0500075.pdf Vahid Nourani Ali Davanlou Tajbakhsh Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, P.O. Box 51666, Tabriz, Iran Vahid Nourani Department of Civil Engineering, Near East University, P.O. Box 99138, Nicosia, North Cyprus, Mersin 10, Turkey Amir Molajou (corresponding author) Department of Water Resources Engineering, Faculty of Civil Engineering, Iran University of Science and Technology, Tehran, Iran E-mail: amolajou@yahoo.com