بررسی تأثیر عناصر اقلیمی بر افزایش دقت روش شبکه عصبی مصنوعی در پیش‌بینی خشک‌سالی منطقه یزد

Drought is a natural feature of the climate condition, and its recurrence is inevitable. The main purpose of this research is to evaluate the effects of climatic factors on prediction of drought in different areas of Yazd based on artificial neural networks technique. In most of the meteorological stations located in Yazd area, precipitation is the only measured factor while generally in synoptic meteorological stations in addition to precipitation some other variables including maximum and mean temperature, relative humidity, wind speed, dominant wind direction and the amount of evaporation are also available. In this research it was tried to evaluate the role of the type and number of meteorological factor (as inputs of ANN model) on accuracy of ANN based drought prediction. Research area is a part of Yazd province containing only one synoptic and 13 non-synoptic meteorological stations. Three-year moving average of monthly precipitation was the main input of the models in all stations. The type of ANN used in this study was time lag recurrent network (TLRN), a dynamic architecture which was selected by evaluation of different types of ANN in this research. What was predicted is the three-year moving average of monthly precipitation of the next year, which is the main factor to evaluate drought condition one year before it occurs. For the Yazd synoptic meteorological station, several combinations of input variables was evaluated and tested to find the most relevant type of input variables for prediction of drought. However, for other 13 stations precipitation data was the only variable to use in ANN models for this purpose. Results in all stations were satisfactory, even where only one input (precipitation) was used to the models, although the level prediction accuracy was different from station to station. Result taken from this research, indicates high flexibility of ANN to cope with poor data condition where it is difficult to get acceptable results by most of the methods.