Effect of Three Gorges Dam on Poyang Lake water level at daily scale based on machine learning

Abstract

Lake water level is an essential indicator of environmental changes caused by natural and human factors. The water level of Poyang Lake, the largest freshwater lake in China, has exhibited a dramatic variation for the past few years, especially after the completion of the Three Gorges Dam (TGD). However, there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level (PLWL) at finer temporal scales (e.g., the daily scale). Here, we used three machine learning models, namely, an Artificial Neural Network (ANN), a Nonlinear Autoregressive model with eXogenous input (NARX), and a Gated Recurrent Unit (GRU), to simulate the daily lake level during 2003-2016. We found that machine learning models with historical memory (i.e., the GRU model) are more suitable for simulating the PLWL under the influence of the TGD. The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods. Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL, the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level. This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.

Simulation of Water Level and Flow of Catastrophic Flood Based on the CNN-LSTM Coupling Network

“…The next step is to increase the catastrophic flood samples and further explore the adaptability and promotion value of the model. The influence of lakes along the river on its water level and flow and accurate simulation are also topics that need further study [29].…”

Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions

“…(2014) with a similar structure to LSTM but fewer parameters and higher operation speed. Considering the main purpose of this study is to explore a new combination of hydrodynamic model and machine learning model, rather than focusing on the types of machine learning, we only choose the commonly used GRU model as a typical representative for the attempt (Gao et al., 2020; Huang et al., 2021; Y. Zhang & Yang, 2021; D. Zhang et al., 2018). GRU is a gating‐mechanism‐based method with an update gate and a reset gate to utilize and modify long‐term memory information.…”

The study on the comparative responses of aquatic vegetation to the interannual water level variation in different hydrologically connected sub‐lakes based on GEE technology

“…The remote‐sensing data were obtained from three datasets, Landsat 5 SR Tier 1 (2000–2012), Landsat 7 SR Tier 1 (2000–2019) and Landsat 8 SR Tier 1 (2013–2019) in the GEE dataset provided by the United States Geological Survey (USGS). They have been geometrically refined, radiometrically calibrated and preprocessed with LaSRC for atmospheric correction (Huang, Xia, et al, 2021). The remote‐sensing images chosen for this study have a spatial resolution of 30 × 30 m, a satellite revisit period of 16d, orbital ranks of 121/40 to 122/40 and a time series of 2000–2019.There are totally 1092 views, including Landsat5 images of 273 views, Landsat7 images of 507 views and Landsat8 images of 507 views.…”

“…The remote-sensing data were obtained from three datasets, Landsat 5 SR Tier 1 (2000-2012), Landsat 7 SR Tier 1 (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019) and Landsat 8 SR Tier 1 (2013)(2014)(2015)(2016)(2017)(2018)(2019) in the GEE dataset provided by the United States Geological Survey (USGS). They have been geometrically refined, radiometrically calibrated and preprocessed with LaSRC for atmospheric correction (Huang, Xia, et al, 2021)…”