Dust Harvesting Centers, Reinforced Regression Tree Model, Sensitivity Mapping, East of Iran

Due to the drought and land use changes in recent years, the phenomenon of storm dust in Iran is increasing as a dangerous environment. Dust influences climate change and human health, causing serious damage. The subject of this research is to identify and prepare a map of sensitivity of dust source area for controlling and determining the role of each of the factors affecting its occurrence using the regression tree data mining model (BRT) in eastern Iran. For this purpose, at first 147 dust source area were identified in the region and divided into two groups for modeling and evaluation. According to the studies, eight effective factors including land use, geology, slope degree, elevation, normalized vegetation index (NDVI), distance from the river, wind speed and rainfall were identified and the layers of these factors were prepared in GIS environment. To evaluate the results, the ROC curve was used. The results showed that the BRT model with the area under the curve (79.6) had a good performance in producing a dust sensitivity map in the study area. Based on the results of the model, vegetation index, elevation and slope had the most impact on the occurrence of dust in the region.