Assessment of flood vulnerability in a coastal metropolitan city for sustainable environmental using machine learning methods.

Floods are among the most destructive natural disasters, causing significant harm to human lives, infrastructure, and the environment. This study addresses the critical challenge of early flash flood prediction in dynamically vulnerable regions. The primary aim of this research is to develop a robust methodology for assessing flood vulnerability in Chennai, Tamil Nadu, India, using advanced machine learning techniques. To achieve this, we employed two ensemble models-artificial neural network (ANN) and random forest (RF)-within a GIS framework, analyzing data from 280 historical flood sites and twelve flood-related parameters. Information gain ratio and multicollinearity diagnostic tests were applied to identify and quantify the influence of key factors contributing to flood occurrences. The predictive performance of the models was compared using statistical criteria, including the "Friedman" test. The findings revealed that both ANN and RF models effectively simulated flood susceptibility, with ANN categorizing areas as 18% very low, 16% low, 13% moderate, 22% high, and 31% very high in vulnerability, while RF classified 7% as very low, 11% low, 34% moderate, 31% high, and 18% very high. The study highlights actionable strategies, including strengthening drainage systems, adopting regulated construction practices in sensitive zones, implementing early warning systems, and enhancing public awareness. The novelty of this research lies in integrating machine learning models with GIS for flood prediction and comparing ensemble model efficacy in a coastal metropolitan setting. The results provide critical insights for policymakers, local authorities, and disaster management agencies to formulate sustainable mitigation strategies. By ensuring effective collaboration among government bodies, NGOs, and local communities, this study contributes to building resilience against floods in Chennai through infrastructure improvement, proactive planning, and community engagement.