ESSCC: A new multi-scale and extensively applicable evaluation system for the construction of sponge cities

Abstract

Sponge cities are actively being built in China to combat urban flooding, and 30 pilot cities (regions) have been established since 2015, serving as a guide for further research. In order to examine the intrinsic links between urban (regional) characteristics, urban planning, sponge city construction metrics, urban ecological carrying capacity, and sustainable development, this study integrates big data and machine learning. For the evaluation of sponge city construction schemes, we combined Artificial Hierarchical Processing (AHP), Gray Correlation Analysis (GCA), and Back Propagation Neural Networks (BPNN) into a multi-scale model-coupled qualitative and quantitative multiple models coupling system. An evaluation system for sponge cities construction (ESSCC), which can be used on a multilingual system, and a sponge city evaluation index system based on ecological carrying capacity and sustainable development have been developed. The system could be used to advance the assessment and optimization of sponge city construction plans in accordance with local conditions from the perspectives of guaranteeing ecological function, ensuring a baseline for natural resource utilization and environmental safety and quality, safeguarding ecological carrying capacity, and ensuring sustainable development. The system is expected to serve as an effective and scientific basis for plan formulation, and to offer direction and technical support for the promotion of sponge cities.