Advancing Coastal Vulnerability Assessment through Machine Learning: A Comprehensive Approach

Gracy Margret Mary, R.; Sannasiraj, S.A., and Raju, D.K., 2025. Advancing coastal vulnerability assessment through machine learning: A comprehensive approach. Journal of Coastal Research, 41(3), 468–481. Charlotte (North Carolina), ISSN 0749-0208. The study presents a comprehensive analysis of vulnerability factors affecting the Chennai coastline from 2000 to 2022, using satellite imagery covering a 40-km stretch. Three distinct zones, delineated from Kattupalli Port to the Adyar River, reveal significant annual erosion and accretion patterns. Zone 1 exhibits high vulnerability rates and steep slopes, crucial indicators of coastal susceptibility to potential sea-level-rise implications. Wave height dynamics during cyclonic periods and wave dissipation near groins highlight the coastal area's response to varying weather conditions. The transformation of built-up regions post-Kattupalli Port establishment in zone 1 and notable changes in land-use and land-cover mapping underscore the study's importance in identifying vulnerable coastal zones. Using advanced computational models such as support vector machine, random forest, and decision tree, the research achieves accuracy rates exceeding 90%, offering invaluable insights for decision-makers. Zone-specific vulnerabilities, including erosion concerns and elevation issues, are meticulously dissected, informing tailored strategies and interventions. Urgent recommendations for resilient coastal management strategies, including beach restoration and urban planning, emphasize proactive measures to mitigate risks and ensure the region's safety amid evolving climatic conditions.