将强大的机器学习模型与洪水风险评估相结合,并确定风险与驱动因素之间的潜在机制,对于改善洪水管理至关重要。在这项研究中,利用六种机器学习模型进行珠江三角洲洪水风险评估,其中梯度提升决策树(GBDT),极限梯度提升(XGBoost),卷积神经网络(CNN)模型首次应用于该领域。选择了12个指数,并创建了2000个样本点用于模型训练和测试。进行模型的超参数优化以确保公平的比较。由于样本数据集的不确定性,利用记录的淹没热点来验证洪水风险区划图的合理性。在确定最优模型后,调查了不同洪水风险等级的驱动因素。还比较了城乡地区以及沿海和内陆地区,以确定不同最高风险地区的洪水风险机制。结果表明,GBDT在6种模型中表现最好,提供了最合理的洪水风险结果。不同风险水平下的驱动因素比较表明,灾害诱发因素,滋生灾害的环境,随着洪水风险的增加,承灾机构并没有变得更加严重。在风险最高的地区,农村地区的灾害滋生环境比城市地区差,沿海地区的灾害诱发因素比内陆地区严重。此外,数字高程模型(DEM)1天最大降水量(M1DP),道路密度(RD)是三大重要驱动因素,对洪水风险的贡献为52%。本研究不仅拓展了机器学习和深度学习方法在洪水风险评估中的应用,也加深了我们对洪水风险潜在机制的理解,并为更好的洪水风险管理提供了见解。
Integrating powerful machine learning models with flood risk assessment and determining the potential mechanism between risk and the driving factors are crucial for improving flood management. In this study, six machine learning models were utilized for flood risk assessment of the Pearl River Delta, in which the Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) models were firstly applied in this field. Twelve indices were chosen and 2000 sample points were created for model training and testing. Hyperparameter optimization of the models was conducted to ensure fair comparisons. Due to uncertainty in the sample dataset, recorded inundation hot-spots were utilized to validate the rationality of the flood risk zoning maps. After determining the optimal model, the driving factors of different flood risk levels were investigated. Urban and rural areas and coastal and inland areas were also compared to determine the flood risk mechanism in different highest-risk areas. The results showed that the GBDT performed best and provided the most reasonable flood risk result among the six models. A comparison of the driving factors at different risk levels indicated that the disaster-inducing factor, disaster-breeding environment, and disaster-bearing body were not definitely becoming more serious as the flood risk increased. In the highest-risk areas, rural areas were featured by worse disaster-breeding environment than urban areas, and the disaster-inducing factors of coastal areas were more serious than those of inland areas. Moreover, the Digital Elevation Model (DEM), maximum 1-day precipitation (M1DP), and road density (RD) were the top three significant driving factors and contributed 52% to flood risk. This study not only expands the application of machine learning and deep learning methods for flood risk assessment, but also deepens our understanding of the potential mechanism of flood risk and provides insights into better flood risk management.