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This study aims to provide a comprehensive assessment of vulnerability and risk to populations and critical infrastructure along the Andaman coast of Thailand, an area highly susceptible to coastal hazards. We combined the Integrated Valuation of Ecosystem Services and Trade-Offs (InVEST) Coastal Vulnerability Model (CVM) and the Digital Shoreline Assessment System (DSAS), further utilising an Artificial Neural Network (ANN) to integrate and analyse diverse variables. The analysis considered shoreline change rates, erosion-accretion dynamics, regional sea-level rise projections, flooding and inundation patterns, tsunami surge probabilities, cyclone trajectories, and socioeconomic factors, including population density and the distribution of critical infrastructure. InVEST CVM incorporated indices such as wave exposure, geomorphology, and natural habitats, while DSAS provided long-term shoreline change data. Vulnerability was quantified by integrating model outputs into the ANN, accounting for exposure, hazard, sensitivity, and adaptive capacity. Risk was calculated by combining vulnerability with the spatial distribution of population and infrastructure. Results reveal that approximately 35% of the coastline is classified as highly vulnerable (particularly the outward-facing shores in Phang Nga, Ranong, Krabi, and Satun), with erosion rates exceeding –9.8 m per year in certain zones. In contrast, the highest risk is concentrated in densely populated areas, particularly Phuket, having risk scores above 0.8 (on a scale of 0 to 1). Less-populated but highly vulnerable regions could become high-risk zones in the future due to development. The study underscores the importance of early, targeted interventions and integrated planning to strengthen the resilience of Thailand's Andaman coastline amid growing climate-related hazards.
Mukhopadhyay et al. (Wed,) studied this question.