Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China

Hui Zhang,Chong Chen,Jixuan Zhao,V.S Litvinenko

doi:10.1051/e3sconf/202126609005

Hui Zhang, Chong Chen + Show 2 more

Open Access

https://doi.org/10.1051/e3sconf/202126609005

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Abstract

Groundwater level is an important factor in evaluating groundwater resources. Due to numerous non-linear factors, establishing theoretical models is difficult.. Therefore, this paper proposesthe BP (Back Propagation) neural network and the Radial Basis Function (RBF) neural network. The study area is divided into two zones. The R2 (coefficient of determination) and RMSE (Root Mean Squared Error) are used to evaluate the performance. The BP neural network is used to predict groundwater level in the two zones with the R2of0.57 and 0.54, with the RMSE of 0.0804 meters and 0.1864 meters respectively. The RBF neural network is implemented with R2of 0.65 and 0.61, with RMSE of 0.0720 meters and 0.1519 meters, respectively. The results show the RBF neural network performs better than the BP neural network in the accuracy of predicting groundwater level. This study shows the feasibility and superiority of groundwater simulation using neural network.

Highlights

The development of Artificial Neural Networks (ANN) provides more ideas and methods for groundwater research
In 2020, Yan Baizhong et al [4] constructed a multivariate Long Short Term Memory(LSTM) network, taking 13-yeardata from monitoring well J1, Daiyue District, Tai'an City as the research object, and exploring the application of LSTM network in groundwater level prediction,and Multivariate LSTM network is superior to the univariate LSTM network and the BP neural network.The neural network proposed in these studies for the corresponding study area is of great significance for predicting the depth of groundwater in similar environments in the future
Cheng Yishanet al. [5] used BP neural network model to forecast the grade of inorganic scale produced in the process of oil field water injection, which provides a method with high precision, less data demand and less calculation time

Summary

Introduction

The development of Artificial Neural Networks (ANN) provides more ideas and methods for groundwater research. In 2002, Fu Qiang et al [2] optimized the BP neural network model with the momentum term learning rules, and conducted simulation experiments on the depth of groundwater in the rice irrigation area. In 2020, Yan Baizhong et al [4] constructed a multivariate Long Short Term Memory(LSTM) network, taking 13-yeardata from monitoring well J1, Daiyue District, Tai'an City as the research object, and exploring the application of LSTM network in groundwater level prediction,and Multivariate LSTM network is superior to the univariate LSTM network and the BP neural network.The neural network proposed in these studies for the corresponding study area is of great significance for predicting the depth of groundwater in similar environments in the future. Kong Xiangchao [6] used the GA-BP model to study the groundwater pollution in the process of oil development in Northern Shanxi Province of China, and established a prediction system between oil development pollution and groundwater environmental impact, which reduced the workload while providing effective data results

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Conclusion