Prediction and Machine Learning Analysis of Urban Waterlogging Risks in High-Density Areas From the Perspective of the Built Environment: A Case Study of Shenzhen, China

Abstract

With the continuous advance of big data and artificial intelligence technologies, various data-driven machine learning algorithms have been widely applied in the studies of urban resilience, particularly in addressing the challenging issue of urban waterlogging. Currently, it is a pressing task to understand the influencing factors of waterlogging from the perspective of built environment, and provide guidance on dynamic monitoring and early alarm services. Focusing on Shenzhen, China, a typical high-density urbanized city, this research constructed a multifactorial dataset encompassing hydrological, meteorological, urban morphology, and waterlogging event data. Then, this research assessed and compared the performance of four mainstream machine learning models—LightGBM, RF, SVR, and BPDNN—in predicting urban waterlogging risks. The results showed that LightGBM had the best accuracy and robustness in predicting waterlogging depths and risk levels in urban areas. The research also employed interpretability algorithm—Shapley Additive Explanations (SHAP)—for decoupling analysis. The results indicated that hydro-meteorological factors (the total rainfall volume and the rainfall lasting time) and several architectural configuration factors (e.g., density of buildings, building congestion degree) are the main influencing factors. In addition, the percentage of water body is vital to waterlogging regulation and retention, especially exhibiting a significant mitigating effect when exceeding 2.5%. This research provides a new technical method for urban waterlogging prediction and reveals the influencing factors and intrinsic mechanisms from the perspective of built environment, which is of great significance for the enhancement of the resilience of high-density cities.

Keywords

Urban Waterlogging / Machine Learning / Model Performance Evaluation / Comparative Research / Model Interpretability Analysis / High-Density City

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2024 Higher Education Press