以潮白河流域 12 个站点为研究对象，选取 12 个站点的未来 12?h 不同预见期的预报降水数据，构建基于支 持 向 量机 (support?vector?machine,SVM)模型、随机森林 (random?forest,RF)模型和多层感知机(multilayer perceptron，MLP) 模型的不同预见期预报降雨校正模型，模型输入为站点对应网格及其周边 8 个网格的降雨预报 数据，模型参数采用贝叶斯优化技术进行估计。利用均方根误差和确定性系数评估各模型对不同预见期预报降 水的校正效果。结果表明：未经校正的原始预报在不同预见期的预报精度均较差；各个误差校正模型在率定期与 验证期对不同预见期降雨均具有较好的校正效果；经 SVM、RF 和 MLP 模型校正后，均方根误差的平均值在率定 期分别降低了：54.2%、50.0% 和 20.8%，在验证期分别降低 42.9%、33.3% 和 14.3%；确定性系数的平均值在率定期 与验证期也均有显著提高；3 个误差校正模型中，SVM 模型表现最优，RF 模型次之。研究成果可为其他流域数值 降雨预报数据校正提供参考。

Rainfall is a direct factor in the formation of flood, and the combination of accurate rainfall forecast data in the long forecast period and hydrological model is the key to improve the accuracy of flood forecast and increase the forecast period, which can strive for a longer emergency response time for flood control and disaster reduction. Rainfall forecast data mainly come from meteorological radar, satellite cloud image and numerical weather forecast products. Although the meteorological observation technology and equipment have made great progress in the past few decades, due to the chaos of atmospheric system, the error of atmospheric initial data and the error of model, the rainfall forecast products inevitably have large errors and limitations, and need to be effectively corrected to improve its accuracy and reliability. The research took 12 stations in Chaobai River basin as the research object, the forecast precipitation data of 12 stations in different forecast periods in the next 12 hours were selected. Rainfall error correction models based on support vector machine, random forest and multilayer perceptron in different forecast periods were constructed. The model input is the rainfall forecast data of the corresponding grid of the station and its surrounding 8 grids, and the model parameters are estimated by Bayesian optimization technology. The root mean square error and deterministic coefficient indexes were used to evaluate the correction effect of each model on precipitation forecast in different forecast periods. The results showed that the prediction accuracy of uncorrected original forecast was poor in different forecast periods. Each error correction model has a good correction effect on rainfall in different forecast periods. After correction by support vector machine model, random forest model and multilayer perceptron model, the average root mean square error decreases by 54.2%, 50.0% and 20.8%, respectively. During the validation period, the reduction was 42.9%, 33.3% and 14.3%, respectively. The average certainty coefficient also increased significantly in both the rate period and the validation period. Among the three error correction models, support vector machine model is the best, followed by random forest model. Based on support vector machine, random forest and multi-layer perceptron model, combined with Bayesian optimization technology, the error correction models of forecast rainfall data in different forecast periods were constructed to correct and analyze the forecast rainfall data of 12 stations in the Chaobai River basin in 12 different forecast periods. The root mean square error and deterministic coefficient were used. The correction effect is good and the accuracy of rainfall forecast is improved, and it can be used as a reference for the numerical rainfall correction of other watershed stations.