Multi-attribute diagnosis of urban flood-bearing bodies based on integrated learning with Stacking–GPR–QPSO coupling

Abstract

Flood-bearing bodies are urban components directly impacted and damaged by disasters. Current methods for attribute identification and diagnosis of flood-bearing bodies, relying on real-time monitoring, are inadequate for pre-disaster forecasting and lack comprehensiveness. To reduce the uncertainty associated with single data sources, a Dual Path Network (DPN) method was employed to extract feature vectors based on multi-source datasets. A meta-classifier was constructed by integrating five base learners using Stacking, optimized by Quantum Particle Swarm Optimization (QPSO)-enhanced Gaussian Process Regression, forming an ensemble learner for predicting urban spatial classification. Utilizing GIS proximity analysis functions, attributes of functional zones, spatial attributes of points of interest (POI), and flood loss were assigned to each flood-bearing body grid. By overlaying urban flood inundation maps, multi-attribute diagnosis of flood-bearing bodies was achieved. The Jinshui District of Zhengzhou, China, is selected as the study area. The results show: (1) Predictions of urban functional zone categories in four other districts of Zhengzhou showed an average accuracy rate of 78.5 % through random sampling point validation. The threshold effect of prediction accuracy at different scales was significant. (2) Simulated flood economic losses for recurrence intervals of 1 year, 5 years, 10 years, 20 years, 50 years, and 100 years exhibited an exponential growth trend. (3) The multiple flood-bearing attributes of each flooded grid can be diagnosed. Finally, the model was effectively verified by simulating and comparing historical data from the “7·20” flood event in Zhengzhou.