Document Type : Research Paper
Irrigation and Development Engineering Department, College of Agriculture and Natural Resources, University of Tehran, Karaj.
Associate Professor, Department of Irrigation and Development Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
Dust has always been one of the most important environmental hazards and has adverse environmental consequences. The purpose of this study is to investigate the relationship between temperature valve variables and dust storms and evaluate the best model for predicting the FDSD index in the west of the country. Using hourly horizontal power data, World Meteorological Organization codes and temperature limit indices including maximum temperature (TXx) and minimum temperature (TNn) on a monthly basis for 14 meteorological stations located in the west of the country with a statistical period of 25 years (1990-2014) and correlated with Tau-Kendall and Pearson correlation coefficients. Map of correlation coefficients to better display the results was prepared by spline method (base radius method) in ArcGIS software. Also, three artificial intelligence models including best neighbor algorithm (KNN), gene expression programming (GEP) and Bayesian network (BN) were evaluated to predict dust. The results showed that dust storms have a positive and significant correlation with temperature limit indices in 14 studied stations, so that the highest Tau-Kendall correlation coefficient with FDSD index is related to the maximum temperature variable in Dogonbadan station with a value of 0.202 and temperature The minimum at Sare-Pole-Zahab station was 0.242. Also, the highest Pearson correlation coefficient with FDSD index for the maximum temperature variable in Dogonbadan station was 0.415 and the minimum temperature in Islamabad station was 0.211. Also, the results of the forecast indicate the proper performance of the KNN method, which is ranked first in 13 stations and the BN method has the best performance in Islamabad station. The results of this study can help to better understand the occurrence of dust storms and study climate relations, as well as reduce the damage caused by this phenomenon in the study area.