Evaluation of Meteorological Signals for Drought Forecasting, Using Regression Methods and Artificial Neural Networks

Drought is one of the destructive natural disasters, which causes most damages to water resources. Drought forecasting can playa crucial role in water management and optimum operation of water resources. In this research work, it was tried to forecast one year ahead drought status with aid the Arterial Nerrral Networks (ANNs) technique and time series of the SPI and ED1 drought indices. In addition to the indices; rainfalls and large scale meteorological index (i.e. SO1 and NAG) were introduced to the ANNE) as inputs that were not effective as the drought indices. The results showed that the selected algorithm was able to forecast the coming six months drought or wet classes correctly in 80% of the months. These amounts for the nine months ahead were 68% and 60% and for the twelve months are 63 % and 58%, which are eenerally considered to be close results. Finally, comparison of the results for the two indices u revealed that the eITors are more frequent in case of the SPI, such that up to 3 and 4 class difference between the forecasted and the observed ones W8,S observed, while for the ED1 it was Jess than 2 classes.

کلیدواژه ها:

Drought Indices ، Meteorological Signals ، Arterial Neural Networks ، Drought Forecasting ، Tehran Province