Global sea level rise (SLR) has emerged as a pressing concern because of its impacts, especially the increased vulnerability of coastal urban areas to flooding. In addition, land subsidence has not generally been examined in flood hazard studies and mitigation efforts due to limited research and data. This study addresses the SLR and land subsidence rate on flood vulnerability in the East Coast of North Sumatra (ECNS) and Medan City to better predict and mitigate flooding in this area.

We integrated spatial modeling within a GIS framework and multi-criteria analysis using the Analytical Hierarchy Process (AHP) approach to develop a spatial model of flood vulnerability. Data for the spatial analysis were derived from multi-sensor satellite imagery, secondary sources from published literature, and field surveys. The study expands the existing framework by incorporating SLR and land subsidence, acknowledging their significant roles in exacerbating flood vulnerability. The analysis also considers several factors, such as slope, land use, and soil type. Future flood intensity scenarios are simulated based on SLR projections. We validated the consistency of the variable weights assigned to the vulnerability analysis using a consistency ratio threshold (<0.1). The satellite-derived sea surface height anomaly data showed a significant trend of SLR in ECNS waters exceeding 4.79 mm/year. The weights assigned to each AHP-derived variable follow a consistent order, starting with the highest weight for slope, followed by land use, land subsidence, SLR, and soil type. The consistency is demonstrated by a CR value of 0.04, which is below the acceptable threshold of 0.1. The established flood vulnerability model was validated by comparing its predictions with recorded flood events in the ECNS and Medan City. Our findings identified more precise vulnerability classes, with 80% of the areas being classified as very high and 20% as highly vulnerable to flooding.