Open AccessIdentification for water quality based on color characteristics
Author(s) -
Jiangang Wang,
Zhengang Zhai,
Yunya Zhu,
Li Zhang,
Xin Fang
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/983/1/012075
Subject(s) - support vector machine , identification (biology) , computer science , moment (physics) , water quality , quality (philosophy) , sampling (signal processing) , artificial intelligence , data mining , machine learning , water resources , pattern recognition (psychology) , computer vision , ecology , biology , philosophy , physics , epistemology , classical mechanics , filter (signal processing)
It’s great significance for protection of water ecological and water resources to identify water quality rapidly and conveniently. In the past time, water quality was test and monitored with traditional laboratory methods, which was hard to meet the requirements of urgent demand. A rapid and convenient method for the identification of water quality based on machine learning was used in this study. By sampling and photographing, the image of water was acquired. Then nine dimensional digital information features of the color information were obtained by the moment method. Based on the historical data and expert experience, a support vector machine (SVM) model was successfully built and well trained. Then the model was verified with the test data, and the accuracy reaches 95%, which proves this method has good effect and high precision. This work will generate fresh insight into water quality identification and contribute to water resources protection.