Next Article in Journal
Equivalent Small Hydro Power: A Simple Method to Evaluate Energy Production by Small Turbines in Collective Irrigation Systems
Next Article in Special Issue
Multi-Objective Parameter Estimation of Improved Muskingum Model by Wolf Pack Algorithm and Its Application in Upper Hanjiang River, China
Previous Article in Journal
Complexity Analysis of Precipitation and Runoff Series Based on Approximate Entropy and Extreme-Point Symmetric Mode Decomposition
Previous Article in Special Issue
Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm
Open AccessArticle

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

by Chen Liang 1, Hongqing Li 2, Mingjun Lei 3 and Qingyun Du 1,4,5,*
1
School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Changjiang Water Resources Protection Institute, Wuhan 430010, China
3
Yangtze River Water Resources Protection Bureau, Wuhan 430010, China
4
Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
5
Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geo-information, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(10), 1389; https://doi.org/10.3390/w10101389
Received: 17 August 2018 / Revised: 21 September 2018 / Accepted: 1 October 2018 / Published: 4 October 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
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. View Full-Text
Keywords: deep learning; LSTM network; water level forecast; the Three Gorges Dam; Dongting Lake deep learning; LSTM network; water level forecast; the Three Gorges Dam; Dongting Lake
Show Figures

Figure 1

MDPI and ACS Style

Liang, C.; Li, H.; Lei, M.; Du, Q. Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network. Water 2018, 10, 1389.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop