Machine learning for water quality classification
In the past years, there has been a lot of interest in water quality and its prediction as there are many pollutants that affect water quality. The techniques provided herein will help us in controlling and reducing the risks of water pollution. In this study, we will discuss concepts related to mac...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2022-08-01
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| Series: | Water Quality Research Journal |
| Subjects: | |
| Online Access: | http://wqrjc.iwaponline.com/content/57/3/152 |
| _version_ | 1797200667363770368 |
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| author | Saleh Y. Abuzir Yousef S. Abuzir |
| author_facet | Saleh Y. Abuzir Yousef S. Abuzir |
| author_sort | Saleh Y. Abuzir |
| collection | DOAJ |
| description | In the past years, there has been a lot of interest in water quality and its prediction as there are many pollutants that affect water quality. The techniques provided herein will help us in controlling and reducing the risks of water pollution. In this study, we will discuss concepts related to machine learning models and their applications for water quality classification (WQC). Three machine learning algorithms, J48, Naïve Bayes, and multi-layer perceptron (MLP), were used for WQC prediction. The dataset used contains 10 features, and in order to evaluate the machine's algorithms and their performance, some accuracy measurements were used. Our study showed that the proposed models can accurately classify water quality. By analyzing the results, it was found that the MLP algorithm achieved the highest accuracy for WQC prediction as compared to other algorithms.
HIGHLIGHTS
Machine learning concept was adopted to analyze the water quality.;
The accuracy of multi-layer perceptron (MLP), is higher than other machine learning algorithms for water quality classification (WQC).;
Extraction of useful and relevant features increases classification accuracy using principal component analysis (PCA).;
The PCA was used for dimensionality reduction and extracts the most dominant water quality features.; |
| first_indexed | 2024-04-24T07:35:17Z |
| format | Article |
| id | doaj.art-8b0b9203e49d4881884f1a2c48da9820 |
| institution | Directory Open Access Journal |
| issn | 2709-8044 2709-8052 |
| language | English |
| last_indexed | 2024-04-24T07:35:17Z |
| publishDate | 2022-08-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Quality Research Journal |
| spelling | doaj.art-8b0b9203e49d4881884f1a2c48da98202024-04-20T07:16:13ZengIWA PublishingWater Quality Research Journal2709-80442709-80522022-08-0157315216410.2166/wqrj.2022.004004Machine learning for water quality classificationSaleh Y. Abuzir0Yousef S. Abuzir1 Civil and Environmental Engineering, Brescia University, Brescia, Italy Faculty of Technology and Applied Sciences, Al-Quds Open University, Ramallah, Palestine In the past years, there has been a lot of interest in water quality and its prediction as there are many pollutants that affect water quality. The techniques provided herein will help us in controlling and reducing the risks of water pollution. In this study, we will discuss concepts related to machine learning models and their applications for water quality classification (WQC). Three machine learning algorithms, J48, Naïve Bayes, and multi-layer perceptron (MLP), were used for WQC prediction. The dataset used contains 10 features, and in order to evaluate the machine's algorithms and their performance, some accuracy measurements were used. Our study showed that the proposed models can accurately classify water quality. By analyzing the results, it was found that the MLP algorithm achieved the highest accuracy for WQC prediction as compared to other algorithms. HIGHLIGHTS Machine learning concept was adopted to analyze the water quality.; The accuracy of multi-layer perceptron (MLP), is higher than other machine learning algorithms for water quality classification (WQC).; Extraction of useful and relevant features increases classification accuracy using principal component analysis (PCA).; The PCA was used for dimensionality reduction and extracts the most dominant water quality features.;http://wqrjc.iwaponline.com/content/57/3/152data miningmachine learningmulti-layer perceptron algorithmpearson's correlation coefficientprincipal component analysis (pca)water quality classification (wqc) |
| spellingShingle | Saleh Y. Abuzir Yousef S. Abuzir Machine learning for water quality classification Water Quality Research Journal data mining machine learning multi-layer perceptron algorithm pearson's correlation coefficient principal component analysis (pca) water quality classification (wqc) |
| title | Machine learning for water quality classification |
| title_full | Machine learning for water quality classification |
| title_fullStr | Machine learning for water quality classification |
| title_full_unstemmed | Machine learning for water quality classification |
| title_short | Machine learning for water quality classification |
| title_sort | machine learning for water quality classification |
| topic | data mining machine learning multi-layer perceptron algorithm pearson's correlation coefficient principal component analysis (pca) water quality classification (wqc) |
| url | http://wqrjc.iwaponline.com/content/57/3/152 |
| work_keys_str_mv | AT salehyabuzir machinelearningforwaterqualityclassification AT yousefsabuzir machinelearningforwaterqualityclassification |