Machine learning-based prediction of sand and dust storm sources in arid Central Asia  

作　　者：Wei Wang Alim Samat Jilili Abuduwaili Philippe De Maeyer Tim Van de Voorde 

机构地区：[1]State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi,People’s Republic of China [2]Research Centre for Ecology and Environment of Central Asia,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi,People’s Republic of China [3]University of Chinese Academy of Sciences,Beijing,People’s Republic of China [4]Department of Geography,Ghent University,Ghent,Belgium [5]Sino-Belgian Joint Laboratory of Geo-Information,Ghent,Belgium [6]Sino-Belgian Joint Laboratory of Geo-Information,Urumqi,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期1530-1550,共21页国际数字地球学报（英文）

基　　金：supported by the National Natural Science Foundation of China(42171014);the UNEPNSFC International Cooperation Project(42161144004);the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20060301);National Natural Science Foundation of China(42071424);the China Scholarship Council(202104910412).

摘　　要：With the emergence of multisource data and the development of cloud computing platforms,accurate prediction of event-scale dust source regions based on machine learning(ML)methods should be considered,especially accounting for the temporal variability in sample and predictor variables.Arid Central Asia(ACA)is recognized as one of the world’s primary potential sand and dust storm(SDS)sources.In this study,based on the Google Earth Engine(GEE)platform,four ML methods were used for SDS source prediction in ACA.Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process.Generally,the results revealed that the random forest(RF)algorithm performed best,followed by the gradient boosting tree(GBT),maximum entropy(MaxEnt)model and support vector machine(SVM).The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction,followed by the normalized difference vegetation index(NDVI).This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions,particularly in ACA.

关 键 词：Susceptibility mapping event scale google earth engine(GEE) remote sensing 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]