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Effect of Three Gorges Dam on Poyang Lake water level at daily scale based on machine learning
Huang Sheng1,2; Xia Jun1,2,3; Zeng Sidong4; Wang Yueling3; She Dunxian1,2
2021-11-01
摘要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.
关键词water level Poyang Lake machine learning Three Gorges Dam Yangtze River
DOI10.1007/s11442-021-1913-1
发表期刊JOURNAL OF GEOGRAPHICAL SCIENCES
ISSN1009-637X
卷号31期号:11页码:1598-1614
通讯作者Huang Sheng(2015huangsheng@whu.edu.cn)
收录类别SCI
WOS记录号WOS:000714930800003
语种英语