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• W2894615779 endingPage "1389" @default.
• W2894615779 startingPage "1389" @default.
• W2894615779 abstract "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." @default.
• W2894615779 created "2018-10-12" @default.
• W2894615779 creator A5041093991 @default.
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• W2894615779 date "2018-10-04" @default.
• W2894615779 modified "2023-10-18" @default.
• W2894615779 title "Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network" @default.
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• W2894615779 doi "https://doi.org/10.3390/w10101389" @default.
• W2894615779 hasPublicationYear "2018" @default.
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