Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM
Rapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people’s property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machine learning approach. Using the simulation res...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/15/7/1397 |
| _version_ | 1797436175827337216 |
|---|---|
| author | Jian Chen Yaowei Li Shanju Zhang |
| author_facet | Jian Chen Yaowei Li Shanju Zhang |
| author_sort | Jian Chen |
| collection | DOAJ |
| description | Rapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people’s property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machine learning approach. Using the simulation results of the hydrodynamic model as the data driver, a neural network structure combining convolutional neural network (CNN) and long and short-term memory network (LSTM) is constructed, taking into account rainfall factors, geographical data, and the distribution of the drainage network. The study was carried out with the central city of Zhoukou as an example. The results show that after the training of the hydrodynamic model and CNN−LSTM neural network model, it can quickly predict the depth of urban flooding in less than 10 s, and the average error between the predicted depth of flooding and the measured depth of flooding does not exceed 6.50%, which shows that the prediction performance of the neural network is good and can meet the seeking of urban emergency flood control and effectively reduce the loss of life and property. |
| first_indexed | 2024-03-09T10:58:49Z |
| format | Article |
| id | doaj.art-072f843961e54930a6d14806afe12286 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T10:58:49Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-072f843961e54930a6d14806afe122862023-12-01T01:26:11ZengMDPI AGWater2073-44412023-04-01157139710.3390/w15071397Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTMJian Chen0Yaowei Li1Shanju Zhang2School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaSchool of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaSchool of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaRapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people’s property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machine learning approach. Using the simulation results of the hydrodynamic model as the data driver, a neural network structure combining convolutional neural network (CNN) and long and short-term memory network (LSTM) is constructed, taking into account rainfall factors, geographical data, and the distribution of the drainage network. The study was carried out with the central city of Zhoukou as an example. The results show that after the training of the hydrodynamic model and CNN−LSTM neural network model, it can quickly predict the depth of urban flooding in less than 10 s, and the average error between the predicted depth of flooding and the measured depth of flooding does not exceed 6.50%, which shows that the prediction performance of the neural network is good and can meet the seeking of urban emergency flood control and effectively reduce the loss of life and property.https://www.mdpi.com/2073-4441/15/7/1397urban floodingconvolutional neural networks (CNN)long and short-term memory networks (LSTM)fast prediction |
| spellingShingle | Jian Chen Yaowei Li Shanju Zhang Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM Water urban flooding convolutional neural networks (CNN) long and short-term memory networks (LSTM) fast prediction |
| title | Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM |
| title_full | Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM |
| title_fullStr | Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM |
| title_full_unstemmed | Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM |
| title_short | Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM |
| title_sort | fast prediction of urban flooding water depth based on cnn lstm |
| topic | urban flooding convolutional neural networks (CNN) long and short-term memory networks (LSTM) fast prediction |
| url | https://www.mdpi.com/2073-4441/15/7/1397 |
| work_keys_str_mv | AT jianchen fastpredictionofurbanfloodingwaterdepthbasedoncnnlstm AT yaoweili fastpredictionofurbanfloodingwaterdepthbasedoncnnlstm AT shanjuzhang fastpredictionofurbanfloodingwaterdepthbasedoncnnlstm |