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Evaluation of Machine Learning Algorithms (RF and SVM) in Producing Flood
Susceptibility Mapping Maroon Watershed
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Mohammad Amin Kiani Asl   , Behzad Moteshaffeh * , Seyed Hussein Roshaan |
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Abstract: (587 Views) |
Floods are one of the most important natural hazards that have caused economic and social damage in
most areas. Numerous climatic, hydrological, geomorphological and geological factors are involved in the
occurrence of floods. Flood analysis, management and control can be done by preparing flood potential
maps. The purpose of this study is to map the flood potential of Maroon watershed using random forest
and support vector machine machine learning methods. For this purpose, 16 effective parameters in flood
occurrence including altitude, slope and aspect, curvature, geological formations, land use, curve number,
rainfall, temperature, stream power index )SPI), topographic wetness index )TWI), distance From stream,
stream density, road distance, road density and NDVI index were considered. The mentioned parameters
were prepared in ArcGIS 10.8, ENVI 5.3 and SAGA GIS 7.2 software environments and then converted to
readable format for R software environment in order to implement the support vector machine and random
forest models. Finally, RF and SVM models were implemented using SDM packages and evaluated using
receiver operating characteristic )ROC). The results showed that RF and SVM models correctly predicted
the flooding map of Maroon Basin with 0.997 and 0.947 percent accuracy, respectively
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| Keywords: Flood management, Flood occurrence, Maroon basin, Random forest, Support vector
machine |
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Full-Text [PDF 1891 kb]
(102 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2023/09/24 | Accepted: 2023/09/11 | Published: 2023/09/11
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