随着全球气候变化的不断加剧和城市化的快速发展,极端降雨过程导致的城市积涝灾害愈演愈烈,已成为世界各国许多城市面临的严重挑战.基于2021年5-8月浙江省诸暨市75个国家自动气象观测站的降雨量数据和典型积水点的积水深度数据,使用深度学习模型长短时记忆网络(Long Short Term Memory,LSTM)构建降雨量与积水深度的关系模型,提供未来间隔15 min的2 h内城市积涝水位预报,并与随机森林(Random Forest,RF)和人工神经网络(Artificial Neural Network,ANN)模型预报结果进行对比.预报结果表明,LSTM使用前4 h的积水与降雨量资料进行未来2 h积水预报的结果最优,均方根误差(RMSE)小于5.6 cm,相关系数(CC)大于0.93,纳什效率系数(NSE)大于0.86,预报效果优于RF和ANN,所构建的积水预报人工智能模型具有较好的预报效果.
Urban waterlogging depth prediction via deep learning approach
With the continuous intensification of global climate change and the rapid urbanization,urban waterlog-ging disasters caused by extreme rainfall events have become increasingly severe,posing a serious challenge for many cities around the world.Here,we propose a deep learning approach to predict urban waterlogging depth,which is based on Long Short-Term Memory(LSTM)and rainfall data from May to August 2021 measured by 75 national automatic meteorological observation stations in Zhejiang's Zhuji city and the water depth data of a typical waterlog-ging site.The relationship between rainfall and waterlogging depth constructed by LSTM provides the next 2-hour ur-ban waterlogging depth forecast with an interval of 15 minutes.When compared with Random Forest(RF)and Arti-ficial Neural Network(ANN)models,the proposed LSTM approach,using water depth and precipitation data over the past 4 hours to predict the next 2-hour waterlogging depth,demonstrates the best performance by lower root mean square error(＜5.6 cm),higher correlation coefficient(＞0.93)and Nash-Sutcliffe efficiency coefficient(＞0.86).It can be concluded that the proposed deep learning approach is feasible and applicable for urban waterlogging depth prediction.
deep learninglong short-term memory(LSTM)urban waterloggingprecipitationwaterlogging depth
万方数据
维普
