This study presents a novel method for optimizing parameters in urban flood models, aiming to address the tedious and complex issues associated with parameter optimization. First, a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model. Next, a principle for dividing urban hydrological response units was introduced, incorporating surface attribute features. The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model, and an artificial neural network (ANN) was employed to identify the sensitive parameters. Finally, a genetic algorithm (GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model. The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient (NSE) of 0.81. Compared to the ANN-GA and K-means-deep neural networks (DNN) methods, the proposed method better characterizes the runoff generation and flow processes. This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

本研究提出了一种优化城市洪水模型参数的新方法，旨在解决与参数优化相关的繁琐而复杂的问题。首先，集成耦合的一维管网径流模型和二维地表径流模型，构建可解释的城市洪水模型。其次，结合地表属性特征，介绍了城市水文响应单元的划分原则。采用K-means算法探索模型中不确定参数的聚类模式，并采用人工神经网络（ANN）识别敏感参数。最后，利用遗传算法（GA）对洪水模型中不同城市土地利用区域的子流域单元的参数阈值进行校准。结果表明，基于K-means-ANN-GA的参数优化方法的平均Nash-Sutcliffe效率系数（NSE）为0.81。与ANN-GA和K-means深度神经网络（DNN）方法相比，该方法更好地表征径流产生和流动过程。这项研究证明了将机器学习技术与物理知识相结合在洪水模型参数优化研究中的巨大潜力。