Coastal flood mitigation under dynamic shoreline change: Integrated modeling approach

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 area experiencing dynamic shoreline changes and the risk of tidal flooding. The aim of this study is to model priority areas for tidal 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 tidal flood vulnerability modelling to derive spatially explicit mitigation priorities at both grid and administrative village levels. The results of the study indicate that the tidal flood risk model identifies the most vulnerable areas, particularly around river mouths and lowland areas, with a vulnerable area of 2,296 hectares. Additionally, priority areas for tidal flood mitigation were identified through the integration of shoreline dynamics and tidal flood potential models, with the analysis revealing 809.70 hectares of high-priority areas located in coastal regions, including the sub-districts of Kapetakan, Gunung Jati, and Losari, as well as 34 villages with high to very high risk, which require focused mitigation strategies to reduce the impacts of abrasion, accretion, and tidal 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 regions. • Over 2,000 hectares in Cirebon were identified as high-risk flood zones. • We introduced a new way to combine shoreline change and tidal flood data using machine learning. • The method uses both satellite imagery and coastal vulnerability models for better accuracy. • Our analysis shows clear patterns of erosion and sea level rise affecting vulnerable areas. • These results give strong evidence to support smarter, targeted flood risk planning.