城市内涝是一种重要的城市灾害，而桥涵积水是城市内涝的主要表现形式之一，容易造成严重的生命伤亡和财 产损失。为了提高桥涵积水深度预测的效率和精度，以当前的气象数据作为输入向量，输出向量是积水深度数据，提出一种桥 涵积水深度预测模型PSO-SVR。首先利用SVR建立积水深度预测模型，再采用PSO对关键参数进行寻优，具有客观性和简 易性的优点。实验结果表明，PSO-SVR模型在桥涵积水深度预测中是有效的，与传统SVR和BP神经网络预测模型相比， PSO-SVR模型对积水深度具有更高的拟合精度。该模型可以为城市内涝预警和应急救援决策提供技术支持。

Urban waterlogging is an important urban disaster, and culvert ponding is one of the main manifestations of urban waterlogging, which can easily cause serious casualties and property losses. In order to improve the efficiency and accuracy of predicting the culvert ponding depth, a predicting model naming PSO-SVR is proposed, which the current meteorological data and waterlogging depth data are selected as input and output vectors respectively. Firstly, a support vector regression prediction model (SVR) for the culvert ponding depth is established, and then the particle swarm optimization algorithm (PSO) is used to optimize key parameters, which has the advantages of objectivity and simplicity. The experimental results indicate that the PSO-SVR model is effective in predicting the depth of culvert ponding. Compared with traditional prediction models of SVR and BP neural network, the PSOSVR model has higher fitting accuracy for waterlogging depth. This model can provide technical support for urban waterlogging warning and emergency rescue decision-making.