Drought Forecasting Using Artificial Wavelet Neural Network Integrated Model (WA-ANN) and Time Series Model (ARIMA)

Drought prediction in water resources systems plays an important role in reducing drought damage. In recent decades, Traditional methods including: fitting and mathematical models have been widely used to predict droughts. The combination of wavelet theory and neural networks has led to the expansion of the wavelet-neural networks. The application of the wavelet as training function in the neural network has recently been identified as a substitute method in neural networks. In these models, the position and scale coefficients of the wavelets are optimized in addition to the weights (Thuillard, 2000). Considering the importance of short-term drought prediction in water resources engineering and the nonlinear characteristics of the SPI series of three months, the purpose of this study is to present an Artificial Wavelet Neural Networks integrated model for predicting short-term drought at Bidestan station in Qazvin plain.
In this research, Multi-Layer Perceptron (MLP), Radial Base Function (RBF), ARIMA time series, as well as Artificial Wavelet Neural Networks integrated model and Multi-layer Perceptron (WA-MLP) and Radial Bonding Function (WA- RBF) were used, which is done by analyzing the time series investigated by the wavelet transformation and the entry of these sub-series into an artificial neural network.
According to previous researches on drought prediction, short-term drought prediction (with the definition of a three-month standard rainfall index) using the combined model of Wavelet-Neural Network and comparing its results with artificial neural network and ARIMA time series models has not been compared. In this paper, five short-term drought prediction models have been compared and a better performance model has been introduced.