Identification for water quality based on color characteristics
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 conveni...
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| Published in | IOP conference series. Earth and environmental science Vol. 983; no. 1; pp. 12075 - 12083 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
01.02.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1755-1307 1755-1315 |
| DOI | 10.1088/1755-1315/983/1/012075 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Fang, Xusheng Wang, Jiangang Zhai, Zhengang Zhu, Yunya Zhang, Li |
| Author_xml | – sequence: 1 givenname: Jiangang surname: Wang fullname: Wang, Jiangang organization: Jiaxing Key Laboratory of Water Ecology Intelligence , China – sequence: 2 givenname: Zhengang surname: Zhai fullname: Zhai, Zhengang organization: Jiaxing Key Laboratory of Water Ecology Intelligence , China – sequence: 3 givenname: Yunya surname: Zhu fullname: Zhu, Yunya organization: Jiaxing Key Laboratory of Water Ecology Intelligence , China – sequence: 4 givenname: Li surname: Zhang fullname: Zhang, Li organization: Jiaxing Key Laboratory of Water Ecology Intelligence , China – sequence: 5 givenname: Xusheng surname: Fang fullname: Fang, Xusheng organization: Jiaxing Key Laboratory of Water Ecology Intelligence , China |
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| Cites_doi | 10.1007/978-3-642-76153-9_5 10.1080/01431161.2013.823524 10.2166/wqrj.2018.025 10.1029/2020GL088946 10.3390/w11112210 10.1016/j.chemosphere.2020.126169 10.3390/rs10081273 10.1016/j.ecoinf.2018.01.005 10.1016/0034-4257(83)90020-2 10.1364/AO.36.008710 10.1080/0143116031000156828 10.1080/10106049.2016.1140818 10.1061/JSUEAX.0000375 10.1016/j.ecolind.2009.11.001 10.1080/01431161.2015.1125555 |
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| SubjectTerms | Color Digital imaging Historical account Image acquisition Laboratory methods Learning algorithms Machine learning Support vector machines Water quality Water resources |
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| Title | Identification for water quality based on color characteristics |
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