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WANG Jielong, YANG Ling, CHEN Yi, SHEN Yunzhong. Prediction of Watershed Groundwater Storage Based on Seasonal Adjustment and NAR Neural Network[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210584
Citation: WANG Jielong, YANG Ling, CHEN Yi, SHEN Yunzhong. Prediction of Watershed Groundwater Storage Based on Seasonal Adjustment and NAR Neural Network[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210584

Prediction of Watershed Groundwater Storage Based on Seasonal Adjustment and NAR Neural Network

doi: 10.13203/j.whugis20210584
Funds:

The National Natural Science Foundation of China (41974002).

  • Received Date: 2022-04-02
    Available Online: 2022-04-21
  • Objectives : Accurate and reliable prediction of watershed groundwater storage can help ensure the sustainable use of a watershed’ s aquifers for urban and rural water supply. Methods : On the basis of precipitation information, gravity recovery and climate experiment (GRACE), and global land data assimilation system (GLDAS), the prediction of groundwater storage is performed by using the seasonal adjustment and non-linear autoregressive(NAR) neural network. Then the NAR model without the seasonal adjustment, the autoregressive (AR) model, and the seasonal autoregressive integrated moving average (SARIMA) model are compared. Results : Taking Changjiang Basin, Lena Basin, Ob Basin and Yenisey Basin as case studies, the results indicate that the deseasonalized precipitation and groundwater using the seasonal adjustment are independent and good fit to AR(1), while laying the foundations of deciding the number of a time delay of the NAR network. The performance of the NAR neural network using the seasonal adjustment falls into the excellent category for each basin and shows superiority over AR model and SARIMA model, with root mean square error (RMSE) less than 1 cm and correlation coefficient more than 0.96. Conclusions :The integration of seasonal adjustment technique and NAR neural network can not only improve the prediction accuracy of groundwater storage, but also reduce the convergence time. The proposed method can effectively forecast the groundwater storage with improved performance due to the seasonal adjustment that reduces the data complexity.
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    [3] Long D, Chen X, Scanlon B R, et al. Have GRACE Satellites Overestimated Groundwater Depletion in the Northwest India Aquifer?[J]. Scientific Reports, 2016, 6:24398
    [4] Khorrami B, Gunduz O. Evaluation of the Temporal Variations of Groundwater Storage and Its Interactions with Climatic Variables Using GRACE Data and Hydrological Models:A Study from Turkey[J]. Hydrological Processes, 2021, 35(3):1-9
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Prediction of Watershed Groundwater Storage Based on Seasonal Adjustment and NAR Neural Network

doi: 10.13203/j.whugis20210584
Funds:

The National Natural Science Foundation of China (41974002).

Abstract: Objectives : Accurate and reliable prediction of watershed groundwater storage can help ensure the sustainable use of a watershed’ s aquifers for urban and rural water supply. Methods : On the basis of precipitation information, gravity recovery and climate experiment (GRACE), and global land data assimilation system (GLDAS), the prediction of groundwater storage is performed by using the seasonal adjustment and non-linear autoregressive(NAR) neural network. Then the NAR model without the seasonal adjustment, the autoregressive (AR) model, and the seasonal autoregressive integrated moving average (SARIMA) model are compared. Results : Taking Changjiang Basin, Lena Basin, Ob Basin and Yenisey Basin as case studies, the results indicate that the deseasonalized precipitation and groundwater using the seasonal adjustment are independent and good fit to AR(1), while laying the foundations of deciding the number of a time delay of the NAR network. The performance of the NAR neural network using the seasonal adjustment falls into the excellent category for each basin and shows superiority over AR model and SARIMA model, with root mean square error (RMSE) less than 1 cm and correlation coefficient more than 0.96. Conclusions :The integration of seasonal adjustment technique and NAR neural network can not only improve the prediction accuracy of groundwater storage, but also reduce the convergence time. The proposed method can effectively forecast the groundwater storage with improved performance due to the seasonal adjustment that reduces the data complexity.

WANG Jielong, YANG Ling, CHEN Yi, SHEN Yunzhong. Prediction of Watershed Groundwater Storage Based on Seasonal Adjustment and NAR Neural Network[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210584
Citation: WANG Jielong, YANG Ling, CHEN Yi, SHEN Yunzhong. Prediction of Watershed Groundwater Storage Based on Seasonal Adjustment and NAR Neural Network[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210584
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