Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms
Abstract The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this wor...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2021-12-01
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| Series: | Human-Centric Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.2991/hcis.k.211203.001 |
| _version_ | 1827321455815163904 |
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| author | Md. Mehedi Hassan Md. Mahedi Hassan Laboni Akter Md. Mushfiqur Rahman Sadika Zaman Khan Md. Hasib Nusrat Jahan Raisun Nasa Smrity Jerin Farhana M. Raihan Swarnali Mollick |
| author_facet | Md. Mehedi Hassan Md. Mahedi Hassan Laboni Akter Md. Mushfiqur Rahman Sadika Zaman Khan Md. Hasib Nusrat Jahan Raisun Nasa Smrity Jerin Farhana M. Raihan Swarnali Mollick |
| author_sort | Md. Mehedi Hassan |
| collection | DOAJ |
| description | Abstract The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model’s feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality, with Variable Importance values of 74.78, 36.805, 81.494, 105.770, 105.166, and 130.173, respectively. |
| first_indexed | 2024-03-07T14:57:50Z |
| format | Article |
| id | doaj.art-d5047b6e0dec46f99df97099ed28b413 |
| institution | Directory Open Access Journal |
| issn | 2667-1336 |
| language | English |
| last_indexed | 2024-04-25T01:06:28Z |
| publishDate | 2021-12-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj.art-d5047b6e0dec46f99df97099ed28b4132024-03-10T12:14:21ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362021-12-0113-4869710.2991/hcis.k.211203.001Efficient Prediction of Water Quality Index (WQI) Using Machine Learning AlgorithmsMd. Mehedi Hassan0Md. Mahedi Hassan1Laboni Akter2Md. Mushfiqur Rahman3Sadika Zaman4Khan Md. Hasib5Nusrat Jahan6Raisun Nasa Smrity7Jerin Farhana8M. Raihan9Swarnali Mollick10Computer Science and Engineering, North Western UniversityComputer Science and Engineering, Bangladesh University of Business and TechnologyBiomedical Engineering, Khulna University of Engineering & TechnologyDepartment of Statistics, University of DhakaComputer Science and Engineering, North Western UniversityComputer Science and Engineering, Ahsanullah University of Science & TechnologyDepartment of Pharmacy, Khulna UniversityComputer Science and Engineering, Bangladesh University of Business and TechnologyDepartment of Pharmacy, University of Development AlternativeComputer Science and Engineering, North Western UniversityComputer Science and Engineering, Northern University of Business & TechnologyAbstract The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model’s feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality, with Variable Importance values of 74.78, 36.805, 81.494, 105.770, 105.166, and 130.173, respectively.https://doi.org/10.2991/hcis.k.211203.001River waterwater quality predictionWQINN |
| spellingShingle | Md. Mehedi Hassan Md. Mahedi Hassan Laboni Akter Md. Mushfiqur Rahman Sadika Zaman Khan Md. Hasib Nusrat Jahan Raisun Nasa Smrity Jerin Farhana M. Raihan Swarnali Mollick Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms Human-Centric Intelligent Systems River water water quality prediction WQI NN |
| title | Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms |
| title_full | Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms |
| title_fullStr | Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms |
| title_full_unstemmed | Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms |
| title_short | Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms |
| title_sort | efficient prediction of water quality index wqi using machine learning algorithms |
| topic | River water water quality prediction WQI NN |
| url | https://doi.org/10.2991/hcis.k.211203.001 |
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