Document Type : Research Paper


1 Assistant Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Ph.D Student, Faculty of Natural Resources, University of Tehran, Karaj, Iran

3 Assistant Professor, Faculty of Environment, University of Tehran, Iran

4 Assistant Professor, International Desert Research Center, University of Tehran, Iran


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.