Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network
| Autor: | Mingjun Lei, Hongqing Li, Chen Liang, Qingyun Du |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Coefficient of determination
lcsh:Hydraulic engineering Mean squared error 0208 environmental biotechnology Geography Planning and Development Flood forecasting 02 engineering and technology Aquatic Science Biochemistry Dongting Lake Long short term memory lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Water Science and Technology Three gorges Hydrology lcsh:TD201-500 Flood myth deep learning water level forecast 020801 environmental engineering Water level Lake water the Three Gorges Dam Environmental science LSTM network |
| Zdroj: | Water, Vol 10, Iss 10, p 1389 (2018) Water Volume 10 Issue 10 |
| ISSN: | 2073-4441 |
| Popis: | To study the Dongting Lake water level variation and its relationship with the upstream Three Gorges Dam (TGD), a deep learning method based on a Long Short-Term Memory (LSTM) network is used to establish a model that predicts the daily water levels of Dongting Lake. Seven factors are used as the input for the LSTM model and eight years of daily data (from 2003 to 2012) are used to train the model. Then, the model is applied to the test dataset (from 2011 to 2013) for forecasting and is evaluated using the root mean squared error (RMSE) and the coefficient of determination (R2). The test shows the LSTM model has better accuracy compared to the support vector machine (SVM) model. Furthermore, the model is adjusted to simulate the situation where the TGD does not exist to explore the dam’s impact. The experiment shows that the water level of Dongting Lake drops conspicuously every year from September to November during the TGD impounding period, and the water level increases mildly during dry seasons due to TGD replenishment. Additionally, the impact of the TGD results in a water level decline in Dongting Lake during flood peaks and a subsequent lagged rise. This research provides a tool for flood forecasting and offers a reference for TGD water regulation. |
| Databáze: | OpenAIRE |
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