Experimental study on the effect of the Three Gorges Project on water level in Lake Poyang

Wu, N.; Luo, Yanhua; Liu, Tonghuan; Zhi-wen, Huang

doi:10.18307/2014.0405

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…Also, the coefficient of determination (R 2 ) is used to measure prediction performance. These two indexes are calculated using Equations (13) and (14) as follows:…”

Section: Model Evaluationmentioning

confidence: 99%

“…For example, Lai et al [13] applied the coupled hydrodynamic analysis model to the middle Yangtze River to compute the variation in the water regime induced by water storage. Wu et al [14] conducted physical model experiments to study the effects of the TGD on the water level in Lake Poyang, which is in the lower reach of the Yangtze River. Jiang and Huang [15] used the Saint Venant equations of river dynamics to study the impacts of the TGD project on the water level of Chenglingji station through modeling of the hydrologic process of the Yangtze River and used this model to predict the water level at Chengilingji station.…”

Section: Introductionmentioning

confidence: 99%

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Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network

Liang

Ming-jun

et al. 2018

Water

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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.

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“…Also, the coefficient of determination (R 2 ) is used to measure prediction performance. These two indexes are calculated using Equations (13) and (14) as follows:…”

Section: Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%