中国防汛抗旱2026，Vol.36Issue(5)：15-21,30,8.DOI:10.16867/j.issn.1673-9264.2026186
基于Wasserstein生成对抗网络过采样的城市洪涝风险智能预测模型
Intelligent prediction model of urban flood risk based on Wasserstein Generative Adversarial Network oversampling
摘要
Abstract
Urban flooding is a critical issue affecting social public safety,and risk prediction serves as an important measure for flood disaster prevention and control.Aiming at the imbalance of inundation depth data in intelligent prediction of urban flood risk,this paper proposes a minority class oversampling method based on Wasserstein Generative Adversarial Network(W-GAN)to generate a balanced deep learning dataset with high,medium,and low-risk samples.Combined with Convolutional Neural Network(CNN),an intelligent prediction model for urban flood risk is established.Taking Haidian Island in Haikou City as the study area,the results show that the number of extremely very low risk samples in the study area is approximately 10 times that of very high risk samples,presenting a significant data imbalance characteristic.The samples generated by the W-GAN algorithm are highly similar to the original samples in features.Compared with the original imbalanced dataset,the optimized balanced dataset effectively improves the prediction accuracy of every risk level,among which the prediction performance of the very high risk level is the most significantly enhanced,with its F1-score increased from 0.712 0 to 0.944 0.This study can provide theoretical support and technical reference for the accurate identification and scientific prevention and control of urban flood risk.关键词
城市洪涝/风险预测/不平衡数据/Wasserstein生成对抗网络/CNNKey words
urban flooding/risk prediction/imbalanced data/Wasserstein Generative Adversarial Network/CNN分类
信息技术与安全科学引用本文复制引用
徐山仑,许红师,王慧亮,杨晨..基于Wasserstein生成对抗网络过采样的城市洪涝风险智能预测模型[J].中国防汛抗旱,2026,36(5):15-21,30,8.基金项目
国家自然科学基金(52579025) （52579025）
河南省自然科学基金杰出青年基金(242300421041). （242300421041）
