A Grey Seasonal Index Model for Forecasting Groundwater Depth of Ningxia Plain

Ziqi Yin,Kai Zhang,Lei Xie

doi:10.1155/2021/6872538

Abstract

Forecasting the depth of groundwater in arid and semiarid areas is a great challenge because these areas are complex hydrogeological environments and the observational data are limited. To deal with this problem, the grey seasonal index model is proposed. The seasonal characteristics of time series were represented by indicators, and the grey model with fractional-order accumulation was employed to fit and forecast different periodic indicators and long-term trends, respectively. Then, the prediction results of the two were combined together to obtain the prediction results. To verify the model performance, the proposed model is applied to groundwater prediction in Yinchuan Plain. The results show that the fitting error of the proposed model is 2.08%, while for comparison, the fitting error of the grey model of data grouping and Holt–Winters model is 3.94% and 5%, respectively. In the same way, it is concluded that the fitting error of groundwater in Weining Plain by the proposed model is 2.26%. On the whole, the groundwater depth in Ningxia Plain including Yinchuan Plain and Weining Plain will increase further.

Highlights

Hybrid support vector machine regression and artificial neural network models were applied to groundwater depth prediction [13]
Ningxia Plain is considered as the largest plain in Ningxia
E GSIM (1,1) model is more suitable for dealing with seasonal fluctuation data

Summary

Introduction

To solve the problem of farmland irrigation, the ancient people of Ningxia built water conservancy projects to divert the Yellow River. In order to provide reference for groundwater management in Ningxia, the grey seasonal index model (GSIM (1,1)) was established to predict the groundwater depth of Ningxia Plain and compared with the results of Holt– Winters model (Holt–Winters) and grey model of data grouping (DGGM (1,1)). Based on different influencing factors, stochastic time series and artificial neural network models are considered to have better effect in groundwater depth assessment [5]. Based on groundwater dynamic data and related factors, the quantile regression method was used to predict groundwater depth [7]. Hybrid support vector machine regression and artificial neural network models were applied to groundwater depth prediction [13]. Us, PSO-GSIM (1,1) is used to predict the groundwater depth of Ningxia Plain

Methodology

Experimental Results

Conclusions