Comparative analysis of different machine learning methods in water quality assessment based on water color

Jiangang Wang; Zhengang Zhai; Yunya Zhu; Xusheng Fang

doi:10.1117/12.2635715

6 May 2022 Comparative analysis of different machine learning methods in water quality assessment based on water color

Jiangang Wang, Zhengang Zhai, Yunya Zhu, Xusheng Fang

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 1225627 (2022) https://doi.org/10.1117/12.2635715
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

It is a common method of using machine learning methods to analyze data in water quality assessment. When faced with different data and different research, different machine learning methods perform differently. In this study, in the water quality assessment problem based on water color, the effects of SVM(support vector machine) and DT(decision tree) method were compared. Through modeling, training and testing, the experimental data of the two methods were obtained. By drawing the confusion matrix and calculating the evaluation indicators, it’s found that the accuracy of DT method was 0.927, which was higher than the SVM method of 0.78. Especially in the F1(harmonic mean) value, in which the DT model was 0.721, while the SVM was only 0.485, so the decision tree method performed better. Although there are still some shortcomings, this research provided a reference for the selection of machine learning methods in water quality assessment.

Citation Download Citation

Jiangang Wang, Zhengang Zhai, Yunya Zhu, and Xusheng Fang "Comparative analysis of different machine learning methods in water quality assessment based on water color", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 1225627 (6 May 2022); https://doi.org/10.1117/12.2635715

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