Fuzzy graph based machine learning optimization for permeable pavement systems in smart cities of Thoothukudi

Frequent hydrometeorological variations have increased the frequency of flooding, posing major risks to infrastructure and public safety. Fuzzy graph theory relies on fundamental concepts such as synchronising and graph domination problems, which have enormous applications in everyday life. In this study, we introduce a novel optimization framework that integrates machine learning with Bipolar Intuitionistic Fuzzy Graphs (BIFG) to optimize stormwater management through permeable pavement systems in the smart cities of Thoothukudi district, India. Bipolar Intuitionistic Fuzzy Graph based Machine Learning Optimization (BIFG-MLO) is used to characterise the ambiguous interactions between metropolitan zones affected by heavy rains, using both positive and negative membership values to indicate drainage effectiveness and waterlogging intensity. Graph dominant concepts and efficient edge detection are used to identify key zones that require infrastructure improvement. Using predictive analytics and a multi-criteria optimization method that ranks smart cities based on dominance data, permeable pavement installation can be prioritised. The results indicate that P&T Colony and Kovilpatti are the most vulnerable cities, making them exemplary choices for implementing permeable pavement. Based on performance evaluation parameters such as accuracy, precision, recall, and F1 score, the proposed model outperforms traditional methods in forecasting flood-prone areas. To improve waterlogging prediction and strategically install permeable pavement systems in Thoothukudi, India's smart city efforts, this study introduces a hybrid optimization framework that employs Bipolar Intuitionistic Fuzzy Graph with Machine Learning Optimization (BIFG-MLO). This framework offers a scalable decision-support system for sustainable urban development in flood-prone locations.