Study on the prediction of mineralization degree of groundwater based on grey prediction model

3rd International Conference on Energy Engineering and Environmental Protection

能源学;生态环境科学

Dong, Xue^1 ; Gu, Sudan^1 ; Li, Tong^1

College of Water Resources and Hydropower, Hebei University of Engineering, Handan, Hebei

056000, China^1

关键词: BP neural network model;    Data informations;    Dependent variables;    GM(1 , n) models;    Grey prediction model;    High water;    Independent variables;    Study areas;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/227/5/052066/pdf
DOI  :  10.1088/1755-1315/227/5/052066

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The mineralization of groundwater has directly effect on the safety of water use of residents and irrigation of crops, especially in the areas with low rainfall, large overproduction and high water consumption in Wei County, Handan. In this paper, it takes Wei County as the study area, takes Qiandianpo and Cai Xiaozhuang, two mineralization over the period 2010-2016 for wellhead measurements as dependent variables, and collects well water level, extraction amount and rainfall to be as independent variables, and then establishes the grey prediction model of GM (1, N). The numerical model of groundwater is simulated and forecasted by MATLAB auxiliary software, and the precision of the model is determined. The simulated and forecasted results are compared with the GM (1, N) model and the BP neural network model. It's showed based on the result that the simulation value of GM (1, N) fits best with the actual value, and the precision is the highest. Therefore, the grey GM (1, N) model is feasible and accurate in predicting mineralization of groundwater, and the prediction results of GM (1, N) model are taken as the predicted values for the period 2017-2020 in the area around the two logging heads, such as Qiandianpo, Cai Xiaozhuang. The predicted values show that the degree of mineralization degree of groundwater in Wei County has been on the rise in the past four years, which provides data information for the relevant departments in Wei County to manage, distribute and dispatch groundwater resources.