Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM
A Case Study in Fuzhou City, China
Bibliographic Data
| ID | 15459101 |
|---|---|
| Authors | Yu Liu (0000-0002-0016-2902, Beijing University of Technology), Hao Wang (0000-0003-3392-1562, Beijing University of Technology, corresponding author), Wenwen Feng (0000-0002-7997-3096, Chang'an University), Haocheng Huang (0000-0002-9782-203X, Central South University) |
| Year | 2021 |
| Volume | 18 |
| Issue | 17 |
| Pages | 9287-9287 |
| Publication date | 2021-09-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph18179287 |
| PMID | 34501876 |
| OpenAlex | W3198819506 |
| Language | EN |
| References cited | 34 |
Water level management is an important part of urban water system management. In flood season, the river should be controlled to ensure the ecological and landscape water level. In non-flood season, the water level should be lowered to ensure smooth drainage. In urban areas, the response of the river water level to rainfall and artificial regulation is relatively rapid and strong. Therefore, building a mathematical model to forecast the short-term trend of urban river water levels can provide a scientific basis for decision makers and is of great significance for the management of urban water systems. With a focus on the high uncertainty of urban river water level prediction, a real-time rolling forecast method for the short-term water levels of urban internal rivers and external rivers was constructed, based on long short-term memory (LSTM). Fuzhou City, China was used as the research area, and the forecast performance of LSTM was analyzed. The results confirm the feasibility of LSTM in real-time rolling forecasting of water levels. The absolute errors at different times in each forecast were compared, and the various characteristics and causes of the errors in the forecast process were analyzed. The forecast performance of LSTM under different rolling intervals and different forecast periods was compared, and the recommended values are provided as a reference for the construction of local operational forecast systems
China · Climatology · Current (fluid · Flood myth · Geography · Hydrology (agriculture · Meteorology · Term (time · Water level · Water resource management · Environmental Science · Flood Risk Assessment and Management · Hydrological Forecasting Using AI · Hydrology and Watershed Management Studies · Geology
| Citation velocity | historical |
|---|---|
| Highly cited | No |