A Universal Urban Flood Risk Model Based on Particle-Swarm-Optimization-Enhanced Spiking Graph Convolutional Networks

Xuhong Fang, Jiaye Li (), Mengyao Wang, Aifang Chen, Songdong Shao and Qunfeng Liu ()
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Xuhong Fang: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Jiaye Li: School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
Mengyao Wang: School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
Aifang Chen: School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
Songdong Shao: School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
Qunfeng Liu: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China

Sustainability, 2025, vol. 17, issue 22, 1-24

Abstract: As climate change and urbanization accelerate, urban flooding poses an increasingly severe threat to urban residents and their properties, creating an urgent need for effective solutions to achieve sustainable urban disaster management. While physically based hydrodynamic models can accurately simulate urban floods, they are data- and computational-resource-demanding. Meanwhile, artificial intelligence models driven by data often lack generalizability across different urban areas. To address these challenges, integrating spiking neural networks, graph convolutional networks (GCNs), and particle swarm optimization (PSO), a novel PSO-enhanced spiking graph convolutional neural network (P-SGCN) is proposed. The model is trained on a self-constructed dataset based on social media data, incorporating six representative Chinese cities: Beijing, Shanghai, Shenzhen, Wuhan, Hangzhou, and Shijiazhuang. These cities were selected for their diverse urban and flood characteristics to enhance model generalizability. The P-SGCN significantly outperforms baseline models such as GCN and long short-term memory, achieving an accuracy, precision, recall, and F1 score of 0.846, 0.847, 0.846, and 0.846, respectively. These results indicate our model’s capability to effectively handle data from six cities while maintaining high accuracy. Meanwhile, the model improves single-city performance through transfer learning and offers extremely fast inference with minimal energy consumption, making it suitable for real-time applications. This study provides a scalable and generalizable solution for urban flood risk management, with potential applications in disaster preparedness and urban planning across varied geographic and socioeconomic contexts.

Keywords: universal urban flood risk model; spiking neural network (SNN); graph convolutional network (GCN); particle swarm optimization (PSO); social media (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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