Application of Artificial Neural Networks in Simulating and Forecasting of Meteorological Drought Decile Percentage Index (Case study: Sistan & Balouchestan Province)

Consecutive droughts in Sistan and Baloochestan province cause water resources restriction and this is a very significant problem for this region. In this study, in order to forecast the drought cycle in 9 climatological stations in the province, we used Artificial Neural Networks. The input data were average of annual rainfall data in all stations and also deciles precipitation index, which the first 30 years from 1971 to 2000 used for training the network and the last 8 years from 2001 to 2008 for simulating it. The network consists of Multilayer Perceptron (MLP) and Back Propagation Algorithm (BP) and also sigmoid transfer function. Number of Neurons in hidden layer was 10 with 1 10-1 structure and was calculated based on the lowest RMSE. Then drought prediction was done in neural network with the trained algorithm and without using actual and observed data in 2009 to 2012. Results showed that, the network was able to simulate and forecast DPI index with 97% regression and average RMSE error less than 5%. According to drought indices, results showed that the drought will have an increasing trend in all stations in this region in 2009 to 2011. Therefore, by using this method, drought can be predicted in later years without any need to have actual meteorological data and also can be used in water resources management, drought management and climate changes.