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• W4304098676 abstract "Abstract Floods are catastrophic natural disasters that inundate large areas and cause loss of life and property, property, and crops. The nature and extent of floods are much higher in the lowland plains than in the plateau region through the accumulation and inundation of a higher volume of water from the upland. The flood susceptibility model is essential to identifying the proper inundation zone for socio-cultural, industrial, and human development. This article highlights the flood susceptibility zonation along the Rupnarayan basin in southwestern West Bengal. It carries through the Rarh region, using a new approach by integrating Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques. An integrated model was employed using two or more individual models to obtain the best combination for delineating the flood susceptible zone of the study area. The result reveals that the probability of flood risk is much higher in the lower part of the study area, and in the western part, about 60% of the study area belongs to the moderate to very high class. Cross-validation using the ROC curve implies a good prediction accuracy, and KNN has the highest prediction rate (0.971). Nonetheless, this study recommended adapting several management techniques such as the dam's construction, check dam, embankment, ban on sand mining, change in land-use practices, to resist the severity of flood effect in this study area." @default.
• W4304098676 created "2022-10-10" @default.
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• W4304098676 date "2022-10-10" @default.
• W4304098676 modified "2023-09-30" @default.
• W4304098676 title "Evaluating the performance of flood susceptibility through novel hybrid machine learning approach in tropical plateau basin, India" @default.
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• W4304098676 doi "https://doi.org/10.21203/rs.3.rs-2132552/v1" @default.
• W4304098676 hasPublicationYear "2022" @default.
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