Open AccessWater Treatment Analysis Based on Support Vector Machine Regression Algorithm
Author(s) -
Guan Xiao-man
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1533/2/022054
Subject(s) - support vector machine , computer science , scale (ratio) , process (computing) , regression analysis , field (mathematics) , algorithm , regression , data mining , relevance vector machine , machine learning , artificial intelligence , mathematics , statistics , physics , quantum mechanics , pure mathematics , operating system
In the current sewage treatment process, the cost of sewage treatment is very high. From the practical problems encountered in the water treatment process, starting with the typical amount of scale buildup prediction, an intelligent prediction algorithm of support vector machine regression algorithm is introduced in this paper as the basis of modeling to analyze the prediction of the typical amount of scale buildup issue encountered in the water treatment field. Through empirical analysis, the specific data obtained from the single-factor experiment is introduced into the support vector machine regression algorithm for prediction operation. The experimental results show that the prediction accuracy is significantly improved compared with that of the traditional method.