Integration of fuzzy AHP and explainable AI for effective coastal risk management: A micro-scale risk analysis of tropical cyclones

Abstract

The east coast of India, especially the coastal region of Odisha, is highly threatened by tropical cyclones. This study develops a detailed risk map for tropical cyclones in the coastal districts of Odisha at the micro level, focusing on the assessment of risk factors at the block level. Using a multi-criteria decision making (MCDM) approach, the study considers four primary risk components: Exposure, vulnerability, susceptibility, and mitigation options. The Explainable Artificial Intelligence (XAI) framework, which uses the XGBoost model in conjunction with SHAP values, is applied to identify and elucidate the factors influencing risk levels in 69 blocks. Results indicate that about 65% of the area is at high risk to tropical cyclone, especially in the northeastern and central regions. In particular, 32 blocks are classified as high to very high-risk zones. The study shows a contrast in risk levels, with blocks in the northeast and southeast at higher risk, while blocks in the southern regions such as Ganjam and Puri and in the central parts of Kendrapara and Baleswar districts are at lower risk. The findings from this study are crucial for local authorities to identify vulnerable areas and improve cyclone preparedness and risk management strategies in Odisha.

Keywords

• Tropical cyclone risk
• Multi-criteria decision-making
• Explainable AI approach
• XGBoost model
• Coastal areas
• Block level analysis