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Smart city article details
| Title | Application Of Cnn-Lstm And Internet Of Things In Rainfall Accumulation Prediction At Urban Flooding Sites |
|---|---|
| ID_Doc | 9821 |
| Authors | Niu S. |
| Year | 2023 |
| Published | 2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023 |
| DOI | http://dx.doi.org/10.1109/ICISCAE59047.2023.10393231 |
| Abstract | In order to accurately and quickly predict the trend of water accumulation at urban flooding sites, a combined neural network-based time series prediction model (CNN-LSTM) is proposed to model and predict multivariate water accumulation time series data. This model utilizes convolutional neural network (CNN) to extract spatial features between multivariate data to obtain spatially correlated feature quantities, and long short-term memory (LSTM) to extract temporal correlation between feature quantities to predict the future water level of waterlogging. Taking one month's waterlogging data of Sino-Singapore Tianjin Eco-city as an example, the results show that the CNN-LSTM prediction model can capture the nonlinear relationship between the water level at the waterlogging point and the inputs very well, and it has a better fit to the actual water level, higher accuracy and generalization ability than the CNN, LSTM and the back propagation (BP) neural network. The effectiveness and applicability of this model in the prediction of urban waterlogging is verified, and it can provide a reliable reference for the early warning and preparation of waterlogging points, as well as the development of pre-flood, flood and post-flood management programs. © 2023 IEEE. |
| Author Keywords | CNN; LSTM; Prediction of waterlogging |
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