Fusing Historical Records and Physics-Informed Priors for Urban Waterlogging Susceptibility Assessment: A Framework Integrating Machine Learning, Fuzzy Evaluation, and Decision Analysis

Abstract

Featured Application : What are the main findings? This study proposes a dual-source sample enhancement strategy that integrates Physics-Informed Priors (PI-PRIORS) with HWR, incorporating extreme rainfall scenarios and applying a joint filtering mechanism based on membership, credibility, and impact degrees. This approach systematically extracts high-quality samples and embeds extreme-scenario information into the modeling process. To address the heterogeneity of polygon-based waterlogging risk distributions, a dimension-reduction sampling framework is introduced based on TWD theory. It integrates an MCCM and the CRITIC-TOPSIS method, which integrates the CRITIC (Criteria Importance Through Intercriteria Correlation) method and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, to quantify membership degrees of overlapping risk levels, while also assessing credibility and social influence scores to support robust point-based sampling in spatially complex environments. A MaxEnt (Maximum Entropy) modeling framework—a statistical learning approach rooted in information entropy theory—is developed by integrating variables from natural conditions, social capital, infrastructure, and the built environment. The contributions and directional effects of each factor are quantified, achieving a balance between interpretability and scalability. This framework offers a transferable tool for diverse urban settings and targeted flood mitigation planning. Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall.