Abstract:
This study presents an innovative approach to flood susceptibility mapping in the Sylhet district
of Bangladesh, integrating Geographic Information System (GIS), machine learning
techniques, and the Analytic Hierarchy Process (AHP). The methodology involves the use of
Digital Elevation Model (DEM) data, Landsat imagery, and soil data, complemented by a
Python-based rainfall prediction model. The study employs AHP to weigh various floodcausing criteria and incorporates machine learning for predictive accuracy in rainfall
forecasting. Two separate models are developed using random forest regressor for Sylhet sadar
and kanaighat and the model had a R square value of .6677 and .5953 respectively with
normalized root square mean error of 0.07 and 0.096. The Flood susceptibility map with
prediction model shows an improvement in predicting vulnerable flood zones in comparison
to the flood susceptibility map using historical rainfall data.
