Evaluation the efficiency of using Artificial Neural Networks in predicting meteorological droughts in north-west of Iran - Arash malekian; mahro dehbozorgi; Amir hoshang ehsani (تحقیقات کاربردی علوم جغرافیایی)
Drought is one of the most destructive natural disasters in human societies that cause irreparable impacts on agriculture, environment, society and economics. So, awareness of occurrence of droughts can be effective in reducing losses. In this study, in order to modeling and forecasting drought severity in a 37 year time period (1971-2007) in 21 meteorological stations, located in the cold semi-arid region of north-west Iran, artificial neural networks was used. The input data was annual rainfall data and annual drought precipitation index for all stations that 80% of the data (1971-2000) used for training the network and other 20% (2001-2007) used for testing it and in the next step drought severity predicted for the years 2008 to 2012 by the trained algorithm without using actual and existed data in this period. The appropriate structure for the network, based on Multi Layer Perceptron with three hidden layer, Back Propagation algorithm, Sigmoid transfer function and 10 neurons in middle layer. The results show that the artificial neural networks are well able to predict the non-linear relationship between rainfall and drought as it can simulate drought precipitation index values largely consistent with the real values with more than 97% regression and less than 5% error. So, drought can be predicted by this method in future and also it is useful in water resources management, drought management and climate change.