Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis

Flood resilience assessment is an important step for any community as it gives the actual scenario of its ability to resist and recover from flood disasters. However, operationalising and quantifying resilience is still a challenge. In Pakistan, very limited research has been done to assess community resilience to floods. The present study proposed an integrated flood resilience model called the “capacity-based flood resilience model (CapFlooR-M)”, which is based on, machine learning (ML), geographical information system (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). The CapFlooR-M incorporates four main components—flood hazard susceptibility (Is), coping capacity (Cc), adaptive capacity (Ac), and transformative capacity (Tc). Random Forest (RF) and Support Vector Machine (SVM) models were used to create a flood susceptibility map, and the AHP was used to compute the relative scores of core capacities, such as Cc, Ac, and Tc and their respective maps were generated. Finally, the susceptibility map was integrated with Cc, Ac, and Tc maps via overlay analysis in GIS to develop a flood resilience map. The overall results reveal that the northwestern and southwestern parts (36.64%; 505 km2) of the study area have moderate to very high resilience, while the central and southeastern parts (63.46%; 877 km2) have very low to low resilience. The findings of this novel approach can support policymakers, land use planners, and other relevant stakeholders to build resilience against flood hazards.

Hussain, M., Tayyab, M., Ullah, K., Ullah, S., Rahman, Z. U., Zhang, J., & Al-Shaibah, B. (2023). Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis. Urban Climate, 50, 101589.

This research is supported by the International (Regional) Cooperation and Exchange Programs of the National Natural Science Foundation of China (41961144019); the Key Scientific and Technology Research and Development Program of Jilin Province (20200403074SF, 20180201033SF, 20180201035SF). We also would like to thank the Pakistan Meteorological Department (PMD), Geological Survey of Pakistan, and District Health and Education Departments (Shangla) for providing us with the relevant data and United States Geological survey (USGS) for Landsat 8 and ASTER DEM images.

Elsevier BV

Urban Climate


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