Considering the effects of dust on human health, environment, agriculture, and transportation, it is necessary to investigate dust emissions susceptibility. This study aimed to study the capability of different machine learning models in analyzing land susceptibility to dust emissions. At first, the dust-source areas were identified by examining the frequency of occurrence (FOO) of dusty days using the aerosol optical depth (AOD) of the MODIS sensor from 2000 to 2020 and field surveys. Then, the weighted subspace random forest (WSRF) model in comparison with three benchmark models-general linear model (GLM), boosted regression tree (BRT), and support vector machine (SVM)-was employed to predict land susceptibility to dust emissions and also to determine the importance of dust-drivers. The results revealed that the WSRF outperformed benchmark models. In a nutshell, the values of accuracy, Kappa, and probability of detection for all models were more than 97 %, and also the false alarm rate was less than 1 % for all models. Spatial analysis indicated a greater frequency of dust events in the outskirts of Urmia Lake (mainly in the eastern and southern parts). Furthermore, according to the map of land susceptibility to dust emissions produced by the WSRF model, about 4.5 %, 2.8 %, 1.8 %, 0.8 %, and 0.2 % of the salt land, rangeland, agricultural, dry-farming, and barren lands, respectively, associated with high and very high degrees of dust emissions susceptibility. Therefore, this study provided in-depth insights into the applicability of the ensemble model, WSRF, to precisely map dust emissions susceptibility.
Keywords: MODIS; Machine learning; Remote sensing; Salty aeolian dust; Susceptibility.
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