Urban Waterlogging Risk Warning Based on Long Short-Term Memory Network
The urban waterlogging risk early warning needs to comprehensively consider multiple factors,such as the spatial and temporal characteristics of waterlogging.In order to enhance the early warning level for urban waterlog-ging risk and respond to the potential rainstorm threat in time,this paper presented an early warning method for urban waterlogging risk based on long short-term memory network.Firstly,we used the core density of waterlogging points and the density of impervious surface to determine the spread area of waterlogging,and thus to obtain the spatial dis-tribution and convergence characteristics of waterlogging according to Moran index.Secondly,we created hydrological data sets and rainfall data sets,and then transformed the problem of waterlogging time feature extraction into a problem of supervised learning.After normalization,we input the characteristic variables into the long-term and short-term memory network for fusion,thus determining the evolution trend of waterlogging.Thirdly,we used convolutional neural networks to predict the waterlogging depth in the future and divide the waterlogging risk level.Based on the fu-sion results of multi-source feature data,we used the single factor risk assessment method and one-dimensional Saint-Venant equations to complete the early warning of waterlogging risk.The simulation results show that the proposed method can accurately extract waterlogging characteristics,with high accuracy and efficiency,which can properly han-dle the early warning problem of waterlogging under the threat of extreme rainstorm.
Multi-source information fusionUrban water loggingWaterloggingEarly warningSpatiotemporal feature extraction
NETL
NSTL
万方数据
