Improving index-based coastal vulnerability assessment using machine learning in Oman

Coastal vulnerability assessments are crucial for evaluating the potential impacts of environmental hazards. Traditional methods that typically rely on index-based approaches are often limited by their inability to account for the relative importance of individual parameters. This study integrated machine learning models (Random Forest and XGBoost), which were optimized through Particle Swarm Optimization, with an index-based method to determine the weights of vulnerability parameters using feature importance analysis. The results were then compared with those derived from the Analytical Hierarchy Process (AHP) and Shannon's Entropy, producing comprehensive Coastal Vulnerability Index (CVI) maps for Oman's coastline. The analysis considered eight parameters, categorizing them into five vulnerability levels before calculating the CVI. Regarding the influential parameters, results showed that most areas exhibit moderate vulnerability (43.05 %) for the geomorphology, followed by very high vulnerability (31.79 %). In contrast, most areas showed very low vulnerability for elevation and slope parameters, covering 55.82 % and 81.10 % of the coastline, respectively. Other parameters showed a varied distribution of vulnerability, particularly in major urban areas, including Muscat. The CVI results showed significant differences among models, with the index-based method identifying 14 % of the coastline as highly vulnerable, AHP <1 %, and Shannon's Entropy 20 %. This study highlighted how different methods varied in prioritizing coastal factors, with AHP and Shannon's Entropy assigning higher weights to NSM and relative sea level rise, while machine learning models uncovered nonlinear relationships and provided a more flexible approach. This research underscores the integration of machine learning models with index-based methods for CVI calculation.