Author(s): Karim Abdrabo; Aly Esmaiel; Mohamed Saber; Sameh Kantosh; Tetsuya Sumi; Bahaa Elboshy; Omar Habiba
Linked Author(s): karim Abdrabo
Keywords: Flood Risk Assessment; Analytical Hierarchy Process; Principal component analysis; Rainfall-Runoff-Inundation; Decision support
Abstract: Flood risk mapping forms the basis for disaster risk management and the associated decision-making systems. This process' effectiveness depends on the quality of the input data of both hazard and vulnerability maps and the method utilized. Worldwide, although there are thousands of studies in Flood Vulnerability Assessment (FVA), however, comparisons between both methods regarding their implementation requirements and options regarding indictors' selection, metrics, transformation, weighting, and the difference between their results were scarce with approximately four studies. This study examines and compares the results of flood risk mapping using Statistical and expert-based approaches combined with the 2D rainfall-runoff-inundation (RRI) simulation model in assessing flood risk in urban areas. For flood vulnerability mapping, inductive principal component analysis (PCA) as a statistical and data-driven based approach and deductive Analytical Hierarchy Process (AHP) as an expert-based approach were adapted to conduct a multidimensional vulnerability assessment. Multiple physical, social, and economic datasets (58 indicators) were collected for that purpose. The indicators for each dimension were derived from the relevant literature, consultations with experts, and data availability. The collected data were analyzed by the software platform that offers advanced statistical analysis (SPSS) and the geographic information system (GIS). Regarding the Hazard mapping, remote sensing data processed by GIS and the RRI model was used to simulate the inundation depth for the worst observed event of an urban flood in Alexandria on Nov 4 2015. This research showed the differences between the subjective and objective assessment methods to obtain detailed analysis as a decision-making support system.
DOI: https://doi.org/10.3850/IAHR-39WC252171192022783
Year: 2022