Coastal areas play a crucial role in supporting economic and social activities, especially in archipelagic countries like Indonesia. However, these regions are increasingly vulnerable to environmental threats, including rising sea levels and significant coastal erosion. This study focuses on Cirebon Regency, a coastal region experiencing dynamic shoreline changes and increasing risk of coastal flooding. The aim of this study is to model priority areas for coastal flood risk mitigation using a Random Forest based machine learning approach. The novelty of this research lies in its integrative framework, which combines multi decadal shoreline change analysis, sea level rise related indicators, and coastal flood vulnerability modelling to derive spatially explicit mitigation priorities at both grid and administrative levels. The results ndicate that the coastal flood risk model identifies the most vulnerable areas, particularly around river mouths and lowland areas, with a vulnerable area of 2296 ha. Additionally, priority areas for coastal flood mitigation were identified through the integration of shoreline dynamics with coastal flood potential models. The analysis revealed 809.7 ha of high-priority areas concentrated in coastal zone, particularly the sub-districts of Kapetakan, Gunung Jati, and Losari. In total, 34 villages were classified as having high to very high risk, requiring targeted mitigation strategies to reduce the impacts of erosion, accretion, and coastal flooding. This study provides an operational framework for policymakers to target limited resources toward the most critical coastal zones, supporting proactive, data driven strategies to minimize economic losses and environmental impacts in vulnerable coastal region.
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