Paper ID: 8827

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

Jinginy Zhao1,2, Hai Guo2*, Min Han1, Haoran Tang2 & Xiaoniu Li2

1 Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, China

2 College of Computer Science and Engineering, Dalian Minzu University, 18 LiaoheWest Road, Dalian Development Zone, Dalian 116600, China

Email: guohai@dlnu.edu.cn

 

Abstract

In recent years, the environmental pollution is more and more serious, especially the water pollution is more serious. In this paper, by using the method of gaussian process regression to build sulphate content of lakes prediction model. And using several water quality variables as inputs. The sulphate content and other water quality variables data from 100 stations operated by the lakes along the middle and lower reaches of Yangtze River were used for developing the four models. The water quality data selected consisted of water temperature, transparency, pH, dissolved oxygen conductivity, chlorophyll, total phosphorus, total nitrogen and ammonia nitrogen are used as inputs to the Gaussian process regression models. Experimental results showed that the Gaussian process regression model of an Exponential kernel had the smallest prediction error, with Mean absolute error (MAE) of 5.0464 and Root mean squared error (RMSE) of 7.269, was smaller than the other three Gaussian process regression models. By contrast experiment, the model used in this paper has less error than linear regression, decision tree, support vector regression, Boosting trees, Bagging trees and other models, more suitable for prediction sulphate content in lakes. In this paper, the method can effectively predict sulphate content in water, for the water detection provides a new kind of auxiliary means and methods.

Keywords: Environmental monitoring; Gaussian process regression; sulphate content; water quality modelling.

 

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ISSN: 2338-5502