A hybrid wavelet analysis and adaptive neuro-fuzzy inference system for drought forecasting

Abstract

Drought forecasting plays an important role in the planning and management of water resources systems. In this paper, a hybrid wavelet and adaptive neuro-fuzzy inference system (WANFIS) is proposed for drought forecasting. The WANFIS model was developed by combining two methods, namely a discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) model. To assess the effectiveness of this model, the standardised precipitation index (SPI) was applied for meteorological drought analysis at five rainfalls gauging stations located around the Klang River basin, Malaysia. The SPI drought forecasting capability performance of the WANFIS model is compared with the autoregressive integrated moving average (ARIMA) and ANFIS models using various statistical measures. Comparison of the results reveals that the WANFIS model performs better than the traditional ANFIS and ARIMA models.