Developing decision making framework on built-up site suitability assessment for urban regeneration in the industrial cities of Eastern India

Abstract

Abstract Unprecedented urban growth in developing countries impacts the existing urban planning as well as prospective urban regeneration. Therefore, evaluating the prospective suitable sites for built-up area development is important to make sustainable urban planning through the urban regeneration process in the industrial-based urban area Asansol Municipal Corporation (AMC). Hence, we analyzed the area-specific built-up suitability using machine learning soft-computing techniques: Artificial Neural Network, Random Forest, and Support Vector Machine. The result showed that the edge of the urban center and periphery of the Asansol, Kulti, and Raniganj were found to be very high (21.52%, 19.87%, 26.32%) to high suitable (11.48%, 19%, 27.26%) areas for further urban planning due to vacant land with available services nearby. However, the southern portion, especially along the Damodar River site and the area near the mining sites were found to be low to very low suitable zones due to inadequate service facilities and high pollution. Finally, we proposed a three-tier urban regeneration framework for sustainable built-up development strategies in AMC that helps to achieve the UN’s sustainable development goals-3, 8, 11, 12 and 13. The findings of this study will benefit policymakers by pointing out the ideal areas for suitable built-up area development initiatives in the near future.

Keywords

• Urban growth
• Urban regeneration
• Built-up suitability
• Machine learning
• Sustainable development goals