Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network
Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In thi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Hydrology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5338/9/6/105 |
| _version_ | 1827660154003259392 |
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| author | Raphaël A. H. Kilsdonk Anouk Bomers Kathelijne M. Wijnberg |
| author_facet | Raphaël A. H. Kilsdonk Anouk Bomers Kathelijne M. Wijnberg |
| author_sort | Raphaël A. H. Kilsdonk |
| collection | DOAJ |
| description | Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas. |
| first_indexed | 2024-03-09T23:38:04Z |
| format | Article |
| id | doaj.art-8623e40fba1745fdacf53f85b702a667 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-09T23:38:04Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-8623e40fba1745fdacf53f85b702a6672023-11-23T16:56:37ZengMDPI AGHydrology2306-53382022-06-019610510.3390/hydrology9060105Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural NetworkRaphaël A. H. Kilsdonk0Anouk Bomers1Kathelijne M. Wijnberg2Water Engineering and Management Department, University of Twente, 7500 AE Enschede, The NetherlandsWater Engineering and Management Department, University of Twente, 7500 AE Enschede, The NetherlandsWater Engineering and Management Department, University of Twente, 7500 AE Enschede, The NetherlandsExtreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas.https://www.mdpi.com/2306-5338/9/6/105machine learningsewer modelLSTM neural networkurban sewer flooding |
| spellingShingle | Raphaël A. H. Kilsdonk Anouk Bomers Kathelijne M. Wijnberg Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network Hydrology machine learning sewer model LSTM neural network urban sewer flooding |
| title | Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network |
| title_full | Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network |
| title_fullStr | Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network |
| title_full_unstemmed | Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network |
| title_short | Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network |
| title_sort | predicting urban flooding due to extreme precipitation using a long short term memory neural network |
| topic | machine learning sewer model LSTM neural network urban sewer flooding |
| url | https://www.mdpi.com/2306-5338/9/6/105 |
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