Analysis of Flooding Under Extreme Conditions with Factors Interactions Using Hybrid Machine Learning

Yanfen Geng (), Xinyu Hu, Xiao Huang and Peng Liu
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Yanfen Geng: Southeast University
Xinyu Hu: Southeast University
Xiao Huang: Southeast University
Peng Liu: Southeast University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 6, No 21, 2879-2897

Abstract: Abstract Urban flooding has intensified, with the rapid proliferation of urban buildings recognized as a significant contributing factor in recent years. This study aims to assess the impact of building layout on urban flooding under extreme conditions, a factor historically underestimated during the early stages of urban planning. Using an advanced K-Means-XGBoost hybrid model, this research investigates the interaction between building layouts and localized flooding incidents in urban environments. The findings indicate that dense and compact building designs offer superior resistance to water accumulation in areas characterized by gentle topography, with elongated structures demonstrating even greater efficacy. This configuration results in a significant reduction in overall water depth, resulting in a 29.42% decrease. Low-density and larger buildings exhibit enhanced flood resistance in regions with pronounced surface undulations, particularly those with square-like shapes. In these scenarios, overall water depth is also minimized, with an 18.97% decrease. A decrease in water depth in one area may exacerbate flooding in adjacent regions. Future planning should consider the increased risk of urban flooding in neighboring areas due to water depth decline in a single region. This study provides valuable insights for urban planning and presents a strategic framework for other cities to mitigate flooding risks.

Keywords: Urban Flooding; Building Layout; Machine Learning; Interaction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04096-8

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