dongting lake water level forecast and its relationship with the three gorges dam based on a long short-term memory network

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ID: 158497
2018
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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.
Reference Key
liang2018waterdongting Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Chen Liang;Hongqing Li;Mingjun Lei;Qingyun Du
Journal Journal of food biochemistry
Year 2018
DOI
10.3390/w10101389
URL
http://www.mdpi.com/2073-4441/10/10/1389
Keywords
Deep learning lstm network

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