Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Lu, Shanbo, Li, Wanqiu, Yao, Guobiao, Zhong, Yulong, Bao, Lifeng, Wang, Zhiwei, Bi, Jingxue, Zhu, Chengcheng, and Guo, Qiuying, 2024. The changes prediction on terrestrial water storage in typical regions of China based on neural networks and satellite gravity data. Scientific Reports, 14:16855, doi:10.1038/s41598-024-67611-8.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024NatSR..1416855L,
author = {{Lu}, Shanbo and {Li}, Wanqiu and {Yao}, Guobiao and {Zhong}, Yulong and {Bao}, Lifeng and {Wang}, Zhiwei and {Bi}, Jingxue and {Zhu}, Chengcheng and {Guo}, Qiuying},
title = "{The changes prediction on terrestrial water storage in typical regions of China based on neural networks and satellite gravity data}",
journal = {Scientific Reports},
keywords = {BP, LSTM, Satellite gravity, TWS change, BiLSTM-attention neural network},
year = 2024,
month = jul,
volume = {14},
eid = {16855},
pages = {16855},
abstract = "{Accurate prediction of regional terrestrial water storage change (TWSA)
is of great significance for water resources planning and
management, and early warning of extreme climate disasters.
Aiming at the problem that the conventional methods on
prediction of TWSA time series are difficult to be accurate, the
six typical regions are selected in China as examples, including
the upper reaches of the Yangtze River (UYR), the southwest
region (SWR), the Liaohe River Basin (LRB), the North China
Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl
River Basin (PRB). The mascon product from GRACE/GRACE-FO
provided by CSR is used to extract TWSA time series in six
typical areas. The improved Back Propagation (BP) neural
network, Long Short-Term Memory (LSTM) neural network and the
latest Bidirectional LSTM (BiLSTM-attention) neural network
model based on attention mechanism are proposed to predict and
analyze the regional TWSA. In the experiment, the selection of
the optimal model parameters such as the number of hidden layer
nodes and the number of hidden units of the neural network model
is tested and analyzed in detail. Meanwhile, the model
prediction results are compared with the traditional least
squares method and random forest (RF) prediction method. The
root mean square error (RMSE), determination coefficient
(R$^{2}$), Nash-Sutcliffe efficiency coefficient (NSE) and mean
absolute percentage error (MAPE) were used to evaluate the
accuracy of the predicted results. The results show that the
improved BP, LSTM and Bi-LSTM-attention neural network models
all achieve higher prediction accuracy in UYR and SWR areas.
RMSE is less than 2.641 cm, R$^{2}$ is as high as 0.8 or more,
NSE is above 0.6, and MAPE is within 0.1. Compared with the
least square method, the RMSE of the predicted results from
three neural network decreased by 0.998 cm, 0.700 cm and 0.7563
on average, and the R$^{2}$ increased by 81.75\%, 69.89\% and
72\% on average. Compared with RFML method, the RMSE from three
neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and
R$^{2}$ is increased by 38.20\%, 24.60\% and 27.06\% on average.
NSE and RMSE are improved to varying degrees in the above
regions. It shows that the improved BP, LSTM and BiLSTM-
attention model used can effectively predict TWSA. The research
methods and results in this paper can provide important
reference for the rational utilization of regional water
resources and disaster risk assessment.}",
doi = {10.1038/s41598-024-67611-8},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024NatSR..1416855L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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