Hybrid Wavelet-M5 Modeling in Rainfall-Runoff Process Forecast

Rainfall-runoff process is one of the most important and complex phenomena in the hydrological cycle, therefore different views have been presented for the development of the modeling. Obviously, the recognition of the behavior of the catchment can play an important role in selecting of the appropriate model for saving time on the simulation. Previous studies have shown that the multi-linear models have an acceptable performance in the case of watersheds which usually have a regular rainfall pattern. In this study, the multi-linear Wavelet-M5 model was introduced and the rainfall-runoff process of the Aji Chay catchment was investigated. At first, the main rainfall and runoff time series were decomposed to several sub-time series by the wavelet transform to overcome its non-stationary, then the obtained sub-time series were imposed as input data to M5 model tree to forecast the runoff values and also the results were compared to the other models by the root mean squared error and determination coefficient criteria. The results showed that the performance of the proposed hybrid Wavelet-M5 model increased up to 69% compared to the sole M5 model tree for the Aji Chay catchment.