干旱区生态环境知识资源中心(Arid): Predicting of dust storm source by combining remote sensing,statistic-based predictive models and game theory in the Sistan watershed,southwestern Asia

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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
	Boroughani Mahdi; Pourhashemi Sima; Gholami Hamid; Kaskaoutis Dimitris G
来源期刊	Journal of Arid Land
ISSN	1674-6767
出版年	2021
卷号	13 期号:11 页码:1103-1121
英文摘要	Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been increased.This 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 models.Furthermore,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 theory.For 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 data.In addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index (NDVI),geomorphology and land use were prepared.The performance of two models (WOE and FR)was evaluated using the area under curve (AUC)of the receiver operating characteristic curve.The results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source areas.The 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 classes.Based on Shapley additive explanations (SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's output.Overall,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 dust storms.
英文关键词	potential dust source remote sensing frequency ratio weight of evidence dust emission
类型	Article
语种	英语
收录类别	CSCD
WOS类目	Meteorology & Atmospheric Sciences
CSCD记录号	CSCD:7100997
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/377436
作者单位	Boroughani Mahdi, Research Center for Geoscience and Social Studies, Hakim Sabzevari University, Sazbevar, 9617976487, Iran.; Pourhashemi Sima, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, 9617976487, Iran.; Gholami Hamid, Department of Natural Resources Engineering, University of Hormozgan, Hormozgan, 7916193145, Iran.; Kaskaoutis Dimitris G, Institute for Environmental Research and Sustainable Development, National Observatory of Athens;;Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, ;;, Athens;;Crete, Greece;;Greece 15784;;70013.
推荐引用方式 GB/T 7714	Boroughani Mahdi,Pourhashemi Sima,Gholami Hamid,et al. Predicting of dust storm source by combining remote sensing,statistic-based predictive models and game theory in the Sistan watershed,southwestern Asia[J],2021,13(11):1103-1121.
APA	Boroughani Mahdi,Pourhashemi Sima,Gholami Hamid,&Kaskaoutis Dimitris G.(2021).Predicting of dust storm source by combining remote sensing,statistic-based predictive models and game theory in the Sistan watershed,southwestern Asia.Journal of Arid Land,13(11),1103-1121.
MLA	Boroughani Mahdi,et al."Predicting of dust storm source by combining remote sensing,statistic-based predictive models and game theory in the Sistan watershed,southwestern Asia".Journal of Arid Land 13.11(2021):1103-1121.