Water quality prediction and classification based on principal component regression and gradient boosting classifier approach
Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression technique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821001361 |
| _version_ | 1798037842413223936 |
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| author | Md. Saikat Islam Khan Nazrul Islam Jia Uddin Sifatul Islam Mostofa Kamal Nasir |
| author_facet | Md. Saikat Islam Khan Nazrul Islam Jia Uddin Sifatul Islam Mostofa Kamal Nasir |
| author_sort | Md. Saikat Islam Khan |
| collection | DOAJ |
| description | Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression technique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic index method. Secondly, the principal component analysis (PCA) is applied to the dataset, and the most dominant WQI parameters have been extracted. Thirdly, to predict the WQI, different regression algorithms are used to the PCA output. Finally, the Gradient Boosting Classifier is utilized to classify the water quality status. The proposed system is experimentally evaluated on a Gulshan Lake-related dataset. The results demonstrate 95% prediction accuracy for the principal component regression method and 100% classification accuracy for the Gradient Boosting Classifier method, which show credible performance compared with the state-of-art models. |
| first_indexed | 2024-04-11T21:32:04Z |
| format | Article |
| id | doaj.art-40b81dc141584d4abb56a644ebd75099 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-11T21:32:04Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-40b81dc141584d4abb56a644ebd750992022-12-22T04:01:55ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134847734781Water quality prediction and classification based on principal component regression and gradient boosting classifier approachMd. Saikat Islam Khan0Nazrul Islam1Jia Uddin2Sifatul Islam3Mostofa Kamal Nasir4Department of Computer Science and Engineering, Santosh, Tangail-1902, Bangladesh; Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, BangladeshDepartment of Information and Communication and Technology, Santosh, Tangail-1902, Bangladesh; Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh; Corresponding author at: Department of Information and Communication and Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh.Department of Technology Studies, Endicott College, Woosong University, Daejeon, South KoreaDepartment of Computer Science and Engineering, Santosh, Tangail-1902, Bangladesh; Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, BangladeshDepartment of Computer Science and Engineering, Santosh, Tangail-1902, Bangladesh; Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, BangladeshEstimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression technique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic index method. Secondly, the principal component analysis (PCA) is applied to the dataset, and the most dominant WQI parameters have been extracted. Thirdly, to predict the WQI, different regression algorithms are used to the PCA output. Finally, the Gradient Boosting Classifier is utilized to classify the water quality status. The proposed system is experimentally evaluated on a Gulshan Lake-related dataset. The results demonstrate 95% prediction accuracy for the principal component regression method and 100% classification accuracy for the Gradient Boosting Classifier method, which show credible performance compared with the state-of-art models.http://www.sciencedirect.com/science/article/pii/S1319157821001361Water quality indexPrincipal component regressionClassification algorithmBoxplot analysis |
| spellingShingle | Md. Saikat Islam Khan Nazrul Islam Jia Uddin Sifatul Islam Mostofa Kamal Nasir Water quality prediction and classification based on principal component regression and gradient boosting classifier approach Journal of King Saud University: Computer and Information Sciences Water quality index Principal component regression Classification algorithm Boxplot analysis |
| title | Water quality prediction and classification based on principal component regression and gradient boosting classifier approach |
| title_full | Water quality prediction and classification based on principal component regression and gradient boosting classifier approach |
| title_fullStr | Water quality prediction and classification based on principal component regression and gradient boosting classifier approach |
| title_full_unstemmed | Water quality prediction and classification based on principal component regression and gradient boosting classifier approach |
| title_short | Water quality prediction and classification based on principal component regression and gradient boosting classifier approach |
| title_sort | water quality prediction and classification based on principal component regression and gradient boosting classifier approach |
| topic | Water quality index Principal component regression Classification algorithm Boxplot analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821001361 |
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