With the development of urbanization, global warming, rain island effect and other factors, cities around the world are facing more frequent and intense flood events. In order to deal with the damage caused by urban flood effectively, it is increasingly important to accurately predict and characterize the information of the flood in cities. In recent years, the rise of machine learning methods provides a new technical means for flood prediction. In this study, Naive Bayes (NB) and Random Forest (RF) algorithm were used to forecast the waterlogging point and the waterlogging process at the waterlogging point respectively to achieve the goal of predicting the whole process of urban waterlogging. Compared with the actual result, the four evaluation indexes (P, R, A and F₁) of the NB classification models are 91%, 90.5%, 98.9% and 90.7% respectively, and the three regression indexes (MAE, MRER and RMSE) of the RF regression model were respectively 0.95%, 9.53% and 1.21%. The results demonstrated that the prediction result of NB model for waterlogging point is reliable, and the process of waterlogging predicted by RF model is also consistent with the actual situation, which verify the validity and applicability of the NB model and RF model. This research is expected to provide scientific guidance and theoretical support for urban flood disaster mitigation and relief work.

随着城市化的发展、全球变暖、雨岛效应等因素，世界各地的城市都面临着更加频繁和强烈的洪水事件。为了有效应对城市洪涝灾害造成的损失，准确预测和表征城市洪水信息变得越来越重要。近年来，机器学习方法的兴起为洪水预测提供了新的技术手段。本研究分别采用朴素贝叶斯(NB)和随机森林(RF)算法对涝点和涝点处的涝渍过程进行预测,以达到预测城市内涝全过程的目的。与实际结果相比，四个评价指标（P、R、A、F ₁)的NB分类模型分别为91%、90.5%、98.9%和90.7%，RF回归模型的三个回归指标(MAE、MRER和RMSE)分别为0.95%、9.53%和1.21%。结果表明，NB模型对涝点的预测结果可靠，RF模型预测的涝点过程也与实际​​情况相符，验证了NB模型和RF模型的有效性和适用性。该研究有望为城市防洪减灾救灾工作提供科学指导和理论支持。