Bhareh gharedaghy; amir ghasemzadeh
Abstract
Due to its high environmental diversity, Iran has a high rank in crises caused by natural disasters. Flood as one of the natural disasters, following the rapid growth of cities and climate changes in many regions, has caused severe social and economic, health and environmental damage. For this reason, ...
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Due to its high environmental diversity, Iran has a high rank in crises caused by natural disasters. Flood as one of the natural disasters, following the rapid growth of cities and climate changes in many regions, has caused severe social and economic, health and environmental damage. For this reason, predict of flood susceptibility is so essential that failure to identify flood susceptibility may increase its destructive effects. Recently, with the advancement of remote sensing tools, geographic information, machine learning and statistical models, it is possible to create a more accurate flood susceptibility map. For this purpose, in this research, by using Sentinel satellite images and using the Ensemble approach with six machine learning models, flood susceptibility was predicted in the Karun watershed. Individual models include Generalized Linear Model (GLM), Boosted Regression Tree (BRT), Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MAXENT). The results of this study show that the northeast of Aligudarz city, parts of Durud and Azna in Lorestan province, Khademmirza, Shahrekord and Kiyar in Chaharmahal Bakhtiari province, Dana and Boyer Ahmad in Kohkiloye and Boyer Ahmad province, Semirom city in Isfahan province and the southern border areas of Karun River in Khuzestan province has the highest flood potential in this basin. The results of this research are effective for managers and planners and will prevent development in vulnerable areas and reduce financial and economic losses in the future.
