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River Flood Prediction Based on Physics-Informed Long Short-Term Memory Model
Flooding is one of the major natural disasters. Predicting river water levels and flooding is an effective way of enabling proactive flooding response measures. Although machine learning-based prediction models in prior studies have obtained a low error rate, they do not perform well during the rapid and significant water level rising (i.e., flooding). To provide a better flooding prediction tool, the present study first evaluates the commonly used long short-term memory model and points out the limitation of prior studies. Then, a novel model named physics-informed (PI) LSTM is proposed. The PI-LSTM integrates hydrological knowledge into the neural network as well as extends the current physics-informed river water level prediction neural networks to a recurrent one. Compared with LSTM, PI-LSTM has a better performance in predicting rapid and significant water level rising. The study is expected to increase the accuracy of flooding prediction and provide better decision-making support to agencies responsible for flood forecasting and warning.
River Flood Prediction Based on Physics-Informed Long Short-Term Memory Model
Flooding is one of the major natural disasters. Predicting river water levels and flooding is an effective way of enabling proactive flooding response measures. Although machine learning-based prediction models in prior studies have obtained a low error rate, they do not perform well during the rapid and significant water level rising (i.e., flooding). To provide a better flooding prediction tool, the present study first evaluates the commonly used long short-term memory model and points out the limitation of prior studies. Then, a novel model named physics-informed (PI) LSTM is proposed. The PI-LSTM integrates hydrological knowledge into the neural network as well as extends the current physics-informed river water level prediction neural networks to a recurrent one. Compared with LSTM, PI-LSTM has a better performance in predicting rapid and significant water level rising. The study is expected to increase the accuracy of flooding prediction and provide better decision-making support to agencies responsible for flood forecasting and warning.
River Flood Prediction Based on Physics-Informed Long Short-Term Memory Model
Pan, Xiyu (author) / Mohammadi, Neda (author) / Taylor, John E. (author)
Construction Research Congress 2024 ; 2024 ; Des Moines, Iowa
Construction Research Congress 2024 ; 208-216
2024-03-18
Conference paper
Electronic Resource
English
Long Short-Term Memory (LSTM) Based Model for Flood Forecasting in Xiangjiang River
| Springer Verlag | 2023