基于元胞自动机和深度学习的城市洪涝预报
Rapid urban flood forecasting based on cellular automata and deep learning
白冰 1董飞 1李传奇 2王薇2
作者信息
- 1. 中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京 100038
- 2. 山东大学土建与水利学院,山东济南 250061
- 折叠
摘要
[目的]在全球气候变化及快速城镇化背景下,城市洪涝灾害频发.如何有效开展城市暴雨洪涝预报研究成为城市水文科学研究的前沿课题.城市二维水动力学模型计算速度较慢,难以实现城市洪涝的快速模拟预报.为满足城市洪涝模拟预报对于计算速度及精度的需求[方法]提出一种数值模拟与深度学习相结合的城市洪涝预报方法.利用元胞自动机(CA)模型进行城市洪涝过程模拟,解决深度学习过程所需要的大量数据集问题;利用卷积神经网络(CNN)建立降雨与最大淹没水深、淹没水深序列之间的映射关系来缩短城市洪涝预报需要的时间.[结果]结果显示:易涝位置最大淹没水深值预报的相对误差小于10％,易涝位置淹没水深序列值预报的纳什效率系数大于0.75.[结论]结果表明:该方法模拟精度高、预报速度快,为城市洪涝预报提供了一种新思路.
Abstract
[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.According-ly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrody-namic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limita-tion and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular au-tomata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative er-ror of forecasting the maximum inundation depth in flood-prone locations is less than 10％,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
关键词
城市洪涝/易涝点/元胞自动机/深度学习/卷积神经网络/快速预测Key words
urban flooding/flood-prone location/cellular automata/deep learning/convolutional neural network/rapid forecas-ting引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据