基于K近邻和水动力模型的城市内涝快速预报

利用水动力模型模拟各量级降雨情况下的城市内涝演变过程,以内涝演变过程数据作为K近邻算法机器学习模型的训练集进行模型训练,采用大气数值模式预报降雨驱动经过训练的K近邻算法机器学习模型进行城市内涝快速预报,并以陕西省秦汉新城3场实测降雨检验了模型的预报性能。结果表明:该模型可在17s内快速预测出城市内涝积水,预报内涝积水面积平均误差不超过8%,积水量及积水深度平均误差不超过15%；该模型预报性能较好,可增强城市防灾减灾能力,有效降低生命财产损失。

Rapid forecasting of urban waterlogging based on K-nearest neighbor and hydrodynamic model

The evolution process of urban waterlogging under different levels of rainfall was simulated using a hydrodynamic model, and then the waterlogging evolution process data was used as the training set of the K-nearest neighbor algorithm machine learning model for model training. The rainfall predicted by the atmospheric numerical model was used to drive the trained K-nearest neighbor machine learning model for rapid urban waterlogging forecasting. Taking Qinhan New City in Shaanxi Province as an example, the predictive performance of the model was tested through three measured rainfall events. The results show that the model can quickly predict urban waterlogging within 17 seconds, with an average error of no more than 8% for the predicted waterlogging area, and no more than 15% for the average error of waterlogging amount and depth. The model has good predictive performance and can enhance urban disaster prevention and reduction capabilities, effectively reduce life and property losses.