AI-Driven Decision Support System for Green and Sustainable Urban Planning in Smart Cities

Abstract

Abstract This study focuses on innovative practices in sustainable urban planning, demonstrating significant advancements in key areas such as solar panel efficiency, rainwater harvesting capacity, community garden space, and bike-sharing station accessibility through in-depth experimentation and analysis. The research results show that the energy conversion rate of solar panels reached 25%, surpassing the market standard of 24%, which is crucial for enhancing self-sufficiency in energy in urban areas. The rainwater harvesting system performed well, achieving a capacity of 600 liters per square meter, slightly below the market rate of 650 liters, but still demonstrating significant potential in dense urban environments. Additionally, our project provided 3 square meters of community garden space per resident, exceeding the market average, effectively promoting urban greening and improving residents’ quality of life. In terms of transportation, our experimental model featured 1.5 bike-sharing stations per 1000 residents, better than the market data of 1.2 stations, contributing to the development of sustainable urban transportation. These outcomes not only showcase the potential of sustainable urban planning but also provide practical references and guidance for future urban development.