[A-HW24] Human- and Climate-induced variability in water cycle and (sub)surface water resources

[AHW24-11] GIS Based Multi Criteria Decision Analysis for Mapping Flood Prone Areas in Delhi, India.

*Prakriti Bisht¹、Shray Pathak¹ (1.Indian Institute of Technology Ropar, Punjab, India)

キーワード：Flood Risk Mapping, Analytic hierarchy process (AHP) , Multiple criteria decision analysis (MCDA) , Land use/land cover (LULC)

Flooding is a critical environmental hazard that endangers human lives, disrupts infrastructure, and destabilizes economies globally. In India, districts near riverine systems are particularly susceptible to inundation, especially during the monsoon season. The absence of spatially explicit flood hazard zoning and inadequate preparedness mechanisms exacerbate the frequency and severity of flood-induced damages. This study leverages geographic information systems (GIS) and remote sensing to quantify past flood extents and assess the vulnerability of various land use/land cover (LULC) classes to flooding. A flood risk mapping framework was developed by integrating GIS with the analytical hierarchy process (AHP), a multi criteria decision analysis (MCDA) technique. Four primary flood-inducing parameters—hydrologic, morphometric, permeability, and land cover dynamics—were identified, encompassing 15 sub-criteria such as elevation, slope, proximity to rivers, topographic wetness index (TWI), drainage density, LULC runoff, precipitation, flow accumulation, stream power index (SPI), aspect, geomorphology, and geology. The relative significance of these parameters was systematically weighted using an AHP pairwise comparison matrix (PCM), ensuring methodological rigor and objectivity. A flood risk map was generated through AHP and validated by the receiver operating characteristic (ROC) curve method. The most and least prominent flood-causing criteria were hydrologic and permeability parameters, respectively. A consistency ratio (CR) of 0.070 (<0.1) confirms the reliability of these weights. The AHP model-derived weights reflect the varying significance of the flood-inducing factors, with rainfall (0.27) being the most influential, followed by elevation (0.04), being the least critical factor. Key flood vulnerable zones (FVZs) were identified by integrating these critical factors. The flood risk map has revealed that 6.96% of the area falls under high risk, 57.72% under moderate risk, 1.56% under very high risk, 27.39% under low risk, and 6.37% under very low risk. The flood risk map was further validated with a RADARSAT images-based map for the year 2023. The model's performance, with an area under the curve accuracy, is 72.1%. The qualitative validation showed that the majority of the flooding occurred in areas that can be classified as very highly and highly flood susceptible. The analysis of flood-affected areas based on LULC types provides a comprehensive understanding of the impact of flooding across different land categories. The total flooded area was determined to be 89.2 km². The LULC-based flood impact analysis revealed that croplands (39.45%) were the most extensively affected category, followed by built-up areas (23.19%), vegetation class (17.71%), and water bodies (11.76%). A comparatively smaller area of rangeland (7.89%) was affected. This research emphasizes the urgent need for resilient urban planning and targeted interventions to mitigate future flood risks. It provides clear insights into the natural and human-induced drivers of flood risk, offering valuable guidance for effective hazard mitigation and sustainable urban development.