Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam
Akshayasimha Channarayapatna Harshasimha, C. M. Bhatt
Abstract
Addressing a natural hazard’s complexity is essential in preventing human fatalities and conserving natural ecosystems as natural hazards are varied and unbalanced in both time and place. Therefore, 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 flood inventory map is generated for different flood locations using multiple official reports. To generate the vulnerability maps, a total of nine geo-environmental parameters are chosen as predictors form Maximum Entropy (MaxEnt) machine learning and Analytical Hierarchy Process (AHP). Accuracy assessment of the outputs from MaxEnt is performed using the area under the curve. Similarly, for AHP outputs, the accuracy is tested using the generated inventory map and the AUC. It is observed that topographical wetness index, elevation, and slope are significant for the assessment of flooded areas. Finally, flood hazard maps are generated and a comparative analysis is performed for both methods. According to the study’s findings, The AUC of the flood map generated by MaxEntis 0.83, whereas the AUC of the flood map generated by AHP is 0.76, which means that the flood map generated by MaxEnt is better. From this study, it can be concluded that hazard maps could be a useful tool for local authorities to identify places that are vulnerable to hazards on a large scale.