ChebNet-LSTM Waterlogging Prediction Model for Urban Flooding

SHAN Jing; XUE Fengchang; CHENG Yao

doi:10.13203/j.whugis20250005

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ChebNet-LSTM Waterlogging Prediction Model for Urban Flooding

更新时间：2026-11-05

- ChebNet-LSTM Waterlogging Prediction Model for Urban Flooding
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 6, Pages: 1225-1234(2025)
- 作者机构：
  
  南京信息工程大学气象灾害地理信息工程实验室，江苏南京，210044
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250005
  CLC： P208;TP18
- Received：08 January 2025，
  
  Published：05 June 2025
- 稿件说明：
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单晶,薛丰昌,程瑶.城市内涝ChebNet-LSTM积水预测模型[J].武汉大学学报（信息科学版）,2025,50(6):1225-1234.

SHAN Jing,XUE Fengchang,CHENG Yao.ChebNet-LSTM Waterlogging Prediction Model for Urban Flooding[J].Geomatics and Information Science of Wuhan University,2025,50(6):1225-1234.
单晶,薛丰昌,程瑶.城市内涝ChebNet-LSTM积水预测模型[J].武汉大学学报（信息科学版）,2025,50(6):1225-1234. DOI：10.13203/j.whugis20250005

SHAN Jing,XUE Fengchang,CHENG Yao.ChebNet-LSTM Waterlogging Prediction Model for Urban Flooding[J].Geomatics and Information Science of Wuhan University,2025,50(6):1225-1234. DOI：10.13203/j.whugis20250005

摘要

随着城市化快速发展，极端暴雨事件频发，城市内涝问题日益严重，精准的积水深度预测对防灾减灾至关重要。现有预测方法中，统计模型难以处理复杂时空特征，而传统机器学习方法又缺乏有效的时空关联建模能力。因此，开发能够有效融合时空特征的预测方法具有重要现实意义。ChebNet利用各监测站间的节点关系矩阵挖掘空间关联，长短期记忆网络(long short-term memory network，LSTM)捕捉数据在时间序列上的变化，二者结合可以充分提取积水深度数据中的时空特征，有效解决传统模型难以兼顾空间与时间信息的局限。采用河南省开封市城区2020—2021年15个积水监测站的小时积水深度观测数据、6个气象站的小时降水数据以及15个监测站之间的距离数据，构建ChebNet-LSTM模型，并用该模型对测试集中的监测站点进行积水深度预测，预测结果显示，平均绝对误差为0.62，平均绝对百分比误差为6.23%，均方根误差为1.06。各项指标表明，ChebNet-LSTM模型在预测积水深度时具有较高的准确性和可靠性。与图注意力网络、LSTM、时空图卷积网络等模型相比，ChebNet-LSTM模型展现出较好的性能，能够有效预测积水变化趋势，为城市排水规划和防涝决策提供重要支持。

Abstract

Objectives

With the increasing frequency of extreme rainfall events and rapid urbanization

urban waterlogging has become a growing concern. Accurate prediction of water accumulation depth is crucial for disaster prevention and mitigation. Traditional statistical models struggle to capture complex spatiotemporal features

while conventional machine learning methods lack effective modeling capabilities for spatiotemporal correlations.

Methods

To address these limitations

this paper proposes a ChebNet-LSTM (long short-term memory network) model that effectively integrates spatial and temporal features for improved prediction accuracy. ChebNet leverages the relational matrix between monitoring stations to extract spatial correlations

while LSTM captures temporal patterns in the data. By combining these two approaches

the proposed model fully exploits the spatiotemporal characteristics of water accumulation depth data

overcoming the shortcomings of traditional models that fail to account for both spatial and temporal information.The study utilizes hourly water depth observations from 15 monitoring stations

hourly precipitation data from six meteorological stations

and distance data between the monitoring stations in Kaifeng City

Henan Province

from 2020 to 2021.The ChebNet-LSTM model is constructed using these datasets and subsequently applied to predict water accumulation depths at monitoring stations in the test set.

Results

The experimental results demonstrate that the ChebNet-LSTM model achieves outstanding predictive performance， with the water depth predictions showing mean absolute error of 0.62， mean absolute percentage error of 6.23%， and root mean square error of 1.06. These evaluation metrics conclusively prove that the proposed model maintains high accuracy and reliability in practical water accumulation depth prediction tasks.

Conclusions

Comparative experiments with models such as graph attention network， LSTM， and spatiotemporal graph convolutional network show that ChebNet-LSTM model outperforms these alternatives， effectively predicting waterlogging trends. The findings provide valuable support for urban drainage planning and flood prevention decision-making， and offer a robust tool for managing urban waterlogging risks.

关键词

Keywords

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