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Fuzzy graph based machine learning optimization for permeable pavement systems in smart cities of Thoothukudi
Angel Benjamin1, Angel Dharmakkan2
1Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India. angelbkezia@gmail.com.
Scientific Reports
|October 8, 2025
Summary
This study optimizes stormwater management using Bipolar Intuitionistic Fuzzy Graphs (BIFG) and machine learning. The BIFG-MLO model prioritizes permeable pavement installation in flood-prone smart cities, enhancing urban resilience.
Area of Science:
- Urban planning and infrastructure resilience.
- Application of fuzzy graph theory in environmental management.
- Machine learning for hydrological risk assessment.
Background:
- Increasing hydrometeorological variations elevate flood risks to urban infrastructure and safety.
- Fuzzy graph theory offers tools for complex system analysis with applications in daily life.
- Smart cities require advanced solutions for effective stormwater management.
Purpose of the Study:
- To introduce a novel optimization framework integrating machine learning with Bipolar Intuitionistic Fuzzy Graphs (BIFG) for stormwater management.
- To optimize permeable pavement system implementation in Thoothukudi district's smart cities.
- To enhance urban resilience against flooding through strategic infrastructure development.
Main Methods:
- Development of a Bipolar Intuitionistic Fuzzy Graph based Machine Learning Optimization (BIFG-MLO) framework.
- Characterization of urban zones using positive and negative membership values for drainage and waterlogging.
- Application of graph dominant concepts and edge detection for identifying infrastructure needs.
- Predictive analytics and multi-criteria optimization for prioritizing permeable pavement installation.
Main Results:
- Identification of P&T Colony and Kovilpatti as the most vulnerable cities requiring urgent permeable pavement implementation.
- The BIFG-MLO model demonstrated superior performance over traditional methods in forecasting flood-prone areas.
- Accurate prediction of waterlogging intensity and drainage effectiveness using fuzzy graph characteristics.
Conclusions:
- The proposed BIFG-MLO framework provides a scalable decision-support system for sustainable urban development in flood-prone areas.
- Strategic installation of permeable pavements can significantly mitigate urban flooding risks.
- This research contributes to improving smart city initiatives for enhanced disaster preparedness and urban resilience.
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