Long-Term Rainfall Estimation in Anzali City Using the Hybrid Wavelet-Adaptive Neuro-Fuzzy Inference System Model

Recently, the amount of rainfall underwent serious changes in different areas, particularly in arid and semi-arid regions. Therefore, estimation and pattern recognition of rainfall in a long term period can give sufficient information to hydrologists and water engineers. In this study, for the first time, long-term rainfall pattern in Anzali city for a 67 years period was simulated using a hybrid model so-called “Wavelet-Adaptive Neuro-Fuzzy Inference System” (WANFIS). Rainfalls of 37-, 20- and 10-years period were applied for training, testing and validation of the numerical model, respectively. Firstly, the optimized membership function of the ANFIS network was obtained using the analysis of the numerical results. In other words, the number of optimized membership function was computed to be equal to 8. Then, the various wavelet families were evaluated which the dmey mother wavelet was introduced as the most optimized wavelet family. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WANFIS models were developed. According to the sensitivity analysis, the superior model and effective lags were identified. The superior model estimated the rainfall with high accuracy. For instance, for validation mode of the superior model, the correlation coefficient, scatter index and Nash-Sutcliffe efficiency coefficient were computed to be 0.962, 0.258 and 0.899, respectively.