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Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia

引用

Journal of Arid Land 2021年第11期13卷 1103-1121页

作者： mahdi boroughani Sima POURHASHEMI Hamid GHOLAMI Dimitris G KASKAOUTIS Research Center for Geoscience and Social Studies Hakim Sabzevari UniversitySazbevar ***Iran Faculty of Geography and Environmental Sciences Hakim Sabzevari UniversitySabzevar ***Iran Department of Natural Resources Engineering University of HormozganHormozgan ***Iran Institute for Environmental Research and Sustainable Development National Observatory of AthensAthens 15784Greece Environmental Chemical Processes Laboratory Department of ChemistryUniversity of CreteCrete 70013Greece

Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human *** to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been *** study aims to identify dust source areas in the Sistan watershed(Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia,using remote sensing(RS)and bivariate statistical ***,this study determines the relative importance of factors controlling dust emissions using frequency ratio(FR)and weights of evidence(WOE)models and interpretability of predictive models using game *** this purpose,we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS *** addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index(NDVI),geomorphology and land use were *** performance of two models(WOE and FR)was evaluated using the area under curve(AUC)of the receiver operating characteristic *** results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source *** 55.3%(according to FR)and 62.6%(according to WOE)of the total area were classified as high and very high potential dust sources,while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE,although they predict different fractions of dust potential *** on Shapley additive explanations(SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's ***,combination of statistic-based predictive models(or data mining models),RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions,which can be helpful for mitigation of negative effects of d

关键词： potential dust source remote sensing frequency ratio weight of evidence dust emission

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