Drought monitoring based on SPI with varying timescales using wavelet based hybrid intelligence methods

Drought modeling is an important hydrology issue, and has a key role in risk management, drought readiness and alleviation. Drought time series data consists of nonlinear features and various time scales. Therefore, this study mixed the strengths of the wavelet transformation and several intelligence methods to develop a new method of a hybrid model for their ability to accurately predict future Standardized Precipitation Index (SPI) as drought index. A ۴۰-year precipitation data from the year ۱۹۸۰–۲۰۱۸ was used for the SPI series with ۳, ۹ and ۲۴ months timescales. For improving the applied intelligence approaches efficiency, pre-processing technique was used via wavelet transform (WT). A comparison between the results of single approaches showed that Multilayer Perceptron Firefly Algorithm (MLP-FFA) method led to better outcomes. The results showed that data processing with WT method enhanced the models capability up to ۴۰%. Also, it was observed that the applied methods were more successful in long-term drought modeling. In general, it was found that the hybrid forecasting models were effective forecasting models in short- to long-term drought modeling.

کلیدواژه ها:

Discrete Wavelet Transform ، Drought ، Multilayer Perceptron Firefly Algorithm ، Preprocessing ، Standardized Precipitation Index.