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A Time-Varying Autoregressive Model for Groundwater Depth Prediction

Journal of Hydrology(2022)SCI 1区SCI 2区

Northwest A&F Univ

Cited 3|Views13
Abstract
The nonstationarity of hydrological variables makes the application of autoregressive (AR) models challenging. Therefore, this study introduces a new time-varying AR (TVAR) model in the field of hydrology. Specifically, in this study, we focus the parameter estimation of the TVAR model and exploring the model's performance for predicting groundwater depth. We demonstrate the application of the model to the monthly groundwater depth series obtained on the Guanzhong Plain, China. We summarize the process of parameter estimation of the TVAR model. First, the TVAR model is transformed into the time-invariance regression problem by expanding the time-varying coefficients into a set of Fourier or Legendre basis functions. Then, a fading memory recursive least squares (FMRLS) algorithm is used to estimate the parameters of the regression problem. In this process, the model order and dimension of the basis function are determined by minimizing our proposed improved Bayesian information criterion (IBIC) with a range of dimensions greater than 0. To further demonstrate the effectiveness of the parameter estimation method and the generalizable performance of the model, the method is applied to nonstationary series simulated in statistical experiments. The study results indicate that the TVAR model based on such a parameter estimation process exhibits better prediction performance, lower model complexity and more straight-forward application compared with the autoregressive integrated (ARI) and seasonal ARI (SARI) models. In conclusion, using the TVAR model as an alternative to the time-invariance ARI and SARI models results in a model that is more flexible and suitable for nonstationary groundwater depth prediction.
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Key words
Time-varying autoregressive model,Groundwater depth prediction,Basis functions,Fading memory recursive least squares algorithm,Improved Bayesian information criterion
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要点】:本论文提出了一种时间变异性自回归(TVAR)模型用于预测地下水位深度,该模型在预测性能上优于传统的自回归积分滑动平均(ARI)和季节性自回归积分滑动平均(SARI)模型。

方法】:论文总结了TVAR模型的参数估计方法。

实验】:研究使用了特定数据集对TVAR、ARI和SARI模型进行比较,TVAR模型显示出更优的预测效果。