In this paper, two hybrid artificial intelligence (AI) based models were introduced for rainfall–runoff modeling. In the first model, a genetic fuzzy system (GFS) was developed and evolved for the prediction of watersheds' runoff one time step ahead. In the second model, the wavelet-GFS (WGFS) model, wavelet transform was also used as a data pre-processing method prior to GFS modeling and in this way the main time series of two variables (rainfall and runoff) were decomposed into some multi-frequency time series by the wavelet transform. Then, the GFS was trained using the transformed time series, and finally the runoff discharge was predicted one time step ahead. In addition, to specify the capability and reliability of the proposed WGFS model, multi-step ahead runoff forecasting was also implemented for the watersheds. The obtained results through the application of the models for rainfall–runoff modeling of two distinct watersheds, located in Azerbaijan, Iran showed that the runoff could be better forecasted through the proposed WGFS model than other AI-based models in terms of determination coefficient and root mean squared error criteria in both training and verifying steps.
A new hybrid algorithm for rainfall–runoff process modeling based on the wavelet transform and genetic fuzzy system
Vahid Nourani, Ahmad Tahershamsi, Peyman Abbaszadeh, Jamal Shahrabi, Esmaeil Hadavandi; A new hybrid algorithm for rainfall–runoff process modeling based on the wavelet transform and genetic fuzzy system. Journal of Hydroinformatics 1 September 2014; 16 (5): 1004–1024. doi: https://doi.org/10.2166/hydro.2014.035
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