引用本文: 王杰龙, 杨玲, 陈义, 沈云中. 结合季节调整和NAR神经网络的流域地下水储量预测[J]. 武汉大学学报 ( 信息科学版), 2022, 47(10): 1796-1804.
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, 2022, 47(10): 1796-1804.
 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, 2022, 47(10): 1796-1804.

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

• 摘要: 利用重力场恢复与气候实验卫星反演的陆地水储量和全球陆地数据同化系统（global land data assimilation system，GLDAS）水文模型，从流域降雨分布信息出发，结合季节调整技术和非线性自回归（non-linear autoregressive，NAR）神经网络对流域地下水储量变化进行预测，并与未经过季节调整的NAR神经网络、自回归（autoregressive, AR）模型以及季节性自回归差分移动平均（seasonal autoregressive integrated moving average, SARIMA）模型进行对比分析。以长江流域、勒拿河流域、鄂毕河流域以及叶尼塞河流域为例，结果表明，经过季节调整后的流域降雨和地下水分别服从独立分布和一阶自回归模型，为NAR神经网络时延数的确定提供了新的途径。经过季节调整后的NAR神经网络的预测结果在4个流域的模型表现优于传统的AR模型和SARIMA模型，均方根误差在1 cm以内，相关系数超过0.96。结合季节调整和NAR神经网络提高了流域地下水储量预测精度，减少了训练参数，加快了神经网络的收敛速度。

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. The NAR model without the seasonal adjustment, the autoregressive (AR) model, and the seasonal autoregressive integrated moving average (SARIMA) model are compared.
Results  Taking the Changjiang Basin, the Lena Basin, the Ob Basin and the Yenisey Basin as case studies, the results indicate that the deseasonalized precipitation is independent and the groundwater is good fit to AR(1), which lay the foundations of deciding the number of a time delay of the NAR neural 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 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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