عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Dust phenomenon is one of the most harmful natural disasters that causes major environmental impacts all over the world. In Iran, Zabol region is hardly affected by this kind of environmental disaster. Current study was made with the aim of identifying time characteristics and evaluation of dust prediction possibility in Zabol Station as the most dusty station in the country. In this regard, firstly, the statistical characteristic of the data related to frequency of monthly, seasonal and annual dusty days in Zabol station with statistics data of 41 years were studied and analyzed. Time series process analysis method has been used for definition of time fluctuations of the study element and monthly classification of the dusty days was made by using statistical multivariable cluster analysis method.
Dust prediction has been done by the use of Adaptive Neuro Fuzzy Inference System (ANFIS) through allocating 70 percent of data to education and 30 percent of it to validity determination of the model. The results showed that August and July months are the dustiest months of the year during the statistical period. Based on the made cluster analysis, the months of July and August with the most dusty days have been placed in a separate cluster. The monthly, seasonal and yearly trend in this station is increasing. The prediction results of dust by ANFIS Method shows its high capability in dust prediction. Fuzzy Inference System (FIS) structure determined by four functions in arc form by hybrid training, method, predicts of dust with 93 percent reliability.