水利学报2026，Vol.57Issue(5)：704-715,12.DOI:10.3724/j.slxb.20250474
基于UNet-KAN-SR模型的城市积涝高效智能预报
Efficient and intelligent forecasting of urban waterlogging based on UNet-KAN-SR modeling
摘要
Abstract
With global climate change and accelerating urbanization,urban waterlogging disasters have become increasingly frequent and severe,making rapid waterlogging forecasting a key research focus.Compared with tradi-tional numerical simulation methods,deep-learning-based artificial intelligence(AI)models can significantly improve computational efficiency.However,they often encounter training bottlenecks due to limited GPU memory.To address this,this study proposes an efficient AI urban waterlogging forecasting model named as UNet-KAN-SR.This model first employs the UNet-KAN module to efficiently simulate the spatio-temporal evolution of waterlogging over low-resolution grids,and then leverages the SR(super-resolution)module,along with high-resolution surface infor-mation,to progressively map the low-resolution waterlogging distribution to high-resolution distribution.This spatio-temporal decoupling strategy can ensure simulation accuracy while substantially reducing the computational resources required for training AI models.Experimental results demonstrate that the UNet-KAN-SR model can simulate a 3-hour waterlogging distribution within 3 minutes,achieving a root mean square error(RMSE)of 9 cm and a probabil-ity of detection(POD)of 0.84,demonstrating high accuracy and computational efficiency.Further analysis reveals that the integration of the KAN module can significantly enhance the model's capability to capture nonlinear flood dynamics when compared with common CNN modules,reducing RMSE by 10％.Furthermore,this study finds that incorporating high-resolution features,such as surface topography,building coverage ratio,and land use,can sig-nificantly improve the simulation performance but performance improvement is similar under different feature combi-nations.This indicates that by optimizing the combination of input features during AI model construction,training speed can be enhanced,modeling costs controlled,and efficient intelligent forecasting achieved.关键词
城市内涝预测/深度学习/时空预测/超分辨率模型/北京城市副中心Key words
urban waterlogging forecasting/deep learning/spatio-temporal prediction/super-resolution model/Bei-jing municipal administrative center分类
天文与地球科学引用本文复制引用
陈耀明,李瑞栋,陈骥,倪广恒..基于UNet-KAN-SR模型的城市积涝高效智能预报[J].水利学报,2026,57(5):704-715,12.基金项目
水圈科学与水利工程全国重点实验室项目(sklhse-TD-2024-C01) （sklhse-TD-2024-C01）
国家重点研发计划课题(2022YFC3090604) （2022YFC3090604）
