Evaluation of Integrated Models of Wavelet-Artificial Neural Network and Wavelet-Gene Expression Programming in the Short-Term Drought Prediction

Drought prediction plays an important role in the planning and management of natural resources, water resources and plant water requirements. Wavelet transform is one of the new and highly effective methods for analysing signals and time series. Using the mother wavelet, the standard precipitation index (SPI) signal was analyzed and the results were considered as inputs of the artificial neural network models and the gene expression programming (GEP). Multi-layer perceptron (MLP), radial basis function (RBF), (GEP), as well as the wavelet-artificial neural networks integrated model and multi-layer perceptron (WA-MLP), radial basis function (WA- RBF) and wavelet- gene expression programming (WA- GEP) were used for drought forecasting. The rainfall data collected at the Bidestan Station for a period of 44 years were used on the Shoor Watershed in the Province of Qazvin. Moisture condition was calculated using the SPI in the short-term period of 3 months. To estimate the SPI in each period, the respective amounts were considered from the previous cycles. The results showed that among the six applied models, the WA-GEP predicted the SPI values and the short-term drought condition with a higher accuracy. The WA-GEP model proved to be the best scenario in the validation stage of R2, RMSE, MAE and NS of 0.911, 0.037, 0.022 and 0.845, respectively.