Flood Vulnerability Mapping using MaxEnt machine learning technique and Analytic Hierarchy Process (AHP) for Kamrup Metropolitan, Assam

Addressing natural hazards's complexity is essential in preventing human fatalities and conserving natural ecosystems as natural hazards are varied and unbalanced in both time and place. So, the main objective of this study is to present a Flood Vulnerability Hazard Map and its evaluation for hazard management and land use planning. The inventory map of natural hazard- flood is generated for different Flood locations using multiple official reports. To generate the vulnerability maps, a total number of 7 geo-environmental parameters are chosen as predictors in Maximum Entropy (MaxEnt) machine learning technique and Analytical Hierarchical Process (AHP). The accuracy assessment of the predicted output models from MaxEnt are evaluated using receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. Similarly, for AHP outputs the accuracy was tested using the generated inventory map. It is observed that topographical wetness index, elevation, and annual mean rainfall are significant for detecting Floods. Finally, Flood hazard maps are generated and a comparative analysis was performed for both methods. According to the study's findings, hazard maps could be a useful tool for local authorities to identify places that are vulnerable to hazards on a large scale.