An effective model based on machine learning for water quality prediction after desalination

Qian Yang; Yule Li; Junkai Gao

doi:10.1117/12.2682248

26 May 2023 An effective model based on machine learning for water quality prediction after desalination

Qian Yang, Yule Li, Junkai Gao

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127003D (2023) https://doi.org/10.1117/12.2682248
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

For areas with limited water resources, seawater desalination is widely used as a solution for water supply, especially in coastal cities. In order to predict the water quality after desalination, this paper proposes a water quality prediction model based on the classification algorithm of random forest. Factors such as pH, hardness, solids and so on are selected and used to build this water quality prediction model. Compared with the prediction accuracy of logistic regression model, the result shows that the water quality prediction model established by random forest classification algorithm has a slightly higher accuracy. Random forest algorithm has the characteristics of high classification accuracy, good practicability, and can effectively monitor water quality.

Citation Download Citation

Qian Yang, Yule Li, and Junkai Gao "An effective model based on machine learning for water quality prediction after desalination", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127003D (26 May 2023); https://doi.org/10.1117/12.2682248

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