Gaussian Process Regression for Prediction of Sulfate Content in Lakes of China


  • Jingying Zhao Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023
  • Hai Guo College of Computer Science and Engineering, Dalian Minzu University, 18 LiaoheWest Road, Dalian Development Zone, Dalian 116600,
  • Min Han Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023,
  • Haoran Tang College of Computer Science and Engineering, Dalian Minzu University, 18 LiaoheWest Road, Dalian Development Zone, Dalian 116600,
  • Xiaoniu Li College of Computer Science and Engineering, Dalian Minzu University, 18 LiaoheWest Road, Dalian Development Zone, Dalian 116600,



Gaussian process regression, water quality modelling, sulphate content, Environmental monitoring, machine learning


In recent years, environmental pollution has become more and more serious, especially water pollution. In this study, the method of Gaussian process regression was used to build a prediction model for the sulphate content of lakes using several water quality variables as inputs. The sulphate content and other variable water quality data from 100 stations operated at lakes along the middle and lower reaches of the Yangtze River were used for developing the four models. The selected water quality data, consisting of water temperature, transparency, pH, dissolved oxygen conductivity, chlorophyll, total phosphorus, total nitrogen and ammonia nitrogen, were used as inputs for several different Gaussian process regression models. The experimental results showed that the Gaussian process regression model using an exponential kernel had the smallest prediction error. Its mean absolute error (MAE) of 5.0464 and root mean squared error (RMSE) of 7.269 were smaller than those of the other three Gaussian process regression models. By contrast, in the experiment, the model used in this study had a smaller error than linear regression, decision tree, support vector regression, Boosting trees, Bagging trees and other models, making it more suitable for prediction of the sulphate content in lakes. The method proposed in this paper can effectively predict the sulphate content in water, providing a new kind of auxiliary method for water detection.


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How to Cite

Zhao, J., Guo, H., Han, M., Tang, H., & Li, X. (2019). Gaussian Process Regression for Prediction of Sulfate Content in Lakes of China. Journal of Engineering and Technological Sciences, 51(2), 198-215.