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Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
2020
Scientific Reports
The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a
doi:10.1038/s41598-020-60191-3
pmid:32081935
pmcid:PMC7035287
fatcat:3pbtmjkrf5aj7md2dgsbliy7fq