A new framework integrating flood inundation modeling and multicriteria decision-making for enhanced flood risk mapping

Flood risk mapping is instrumental in guiding land-use decisions, development planning, disaster management, and mitigation strategies. However, the accuracy of such maps relies heavily on the availability of comprehensive data. When such data are lacking, empirical approaches are employed to estimate flood risk. Several recent studies have developed flood risk maps using multicriteria decision-making (MCDM), such as the analytical hierarchy process (AHP). However, flood risk mapping methods using MCDM techniques are zero-dimensional models, and they cannot be associated with a flood of a particular exceedance probability. Notably, flood inundation models can predict floods and help map flood parameters for floods with different return periods. Accordingly, this study proposes a new framework for mapping flood risk for floods with different return periods by integrating inundation maps obtained from a flood simulation model with an MCDM framework in a geographic information system (GIS) environment. The proposed method integrates remote sensing data, hydraulic modeling, and AHP combined with a sensitivity analysis to develop a flood risk map. The applicability of the proposed framework is demonstrated by employing it to create flood risk maps for flood events with different return periods in the East Fork White River (EFWR) in Columbus, Indiana, USA. The results reveal a significant correspondence between high-risk zones identified in the flood risk maps and areas with high values on an available flood damage map of the study area, confirming the efficacy of the proposed framework. This study highlights the potential of the methodology as a valuable tool for generating flood risk maps in areas where comprehensive flood risk assessment data are limited. Additionally, the flexibility of the GIS-based approach allows for the adaptation and application of the methodology to different geographic locations and flood scenarios. Thus, the proposed framework offers a robust and practical approach to flood risk mapping with potential applications in disaster management and land-use planning strategies.