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

• Main Authors: Wang, Jiangang, Zhai, Zhengang, Zhu, Yunya, Fang, Xusheng
• Format: Conference Proceeding
• Language: English
• Published: SPIE 06.05.2022
• ISBN: 9781510655133; 1510655131
• ISSN: 0277-786X
• DOI: 10.1117/12.2635715

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.