Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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 (Autor:in) / Mohammadi, Neda (Autor:in) / Taylor, John E. (Autor:in)
Construction Research Congress 2024 ; 2024 ; Des Moines, Iowa
Construction Research Congress 2024 ; 208-216
18.03.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Long Short-Term Memory (LSTM) Based Model for Flood Forecasting in Xiangjiang River
| Springer Verlag | 2023
| DOAJ | 2022