Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach
One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during su...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9945948/ |
| _version_ | 1797984147200802816 |
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| author | Bilal Aslam Ahsen Maqsoom Ali Hassan Cheema Fahim Ullah Abdullah Alharbi Muhammad Imran |
| author_facet | Bilal Aslam Ahsen Maqsoom Ali Hassan Cheema Fahim Ullah Abdullah Alharbi Muhammad Imran |
| author_sort | Bilal Aslam |
| collection | DOAJ |
| description | One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (a combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945, and PBIAS = −0.64 outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS). |
| first_indexed | 2024-04-11T06:58:00Z |
| format | Article |
| id | doaj.art-95e1b81e653e466d897d27f2ff442f13 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T06:58:00Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-95e1b81e653e466d897d27f2ff442f132022-12-22T04:38:57ZengIEEEIEEE Access2169-35362022-01-011011969211970510.1109/ACCESS.2022.32214309945948Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing ApproachBilal Aslam0https://orcid.org/0000-0001-7308-5285Ahsen Maqsoom1https://orcid.org/0000-0002-3745-516XAli Hassan Cheema2https://orcid.org/0000-0002-6931-9023Fahim Ullah3https://orcid.org/0000-0002-6221-1175Abdullah Alharbi4https://orcid.org/0000-0001-8617-1430Muhammad Imran5https://orcid.org/0000-0002-6946-2591School of Informatics, Computing, Cyber Systems, Northern Arizona University, Flagstaff, AZ, USADepartment of Civil Engineering, COMSATS University Islamabad, Wah Cantt, Islamabad, PakistanDepartment of Civil Engineering, COMSATS University Islamabad, Wah Cantt, Islamabad, PakistanSchool of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD, AustraliaDepartment of Computer Science, Community College, King Saud University, Riyadh, Saudi ArabiaInstitute of Innovation, Science and Sustainability, Federation University, Brisbane, QLD, AustraliaOne of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (a combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945, and PBIAS = −0.64 outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS).https://ieeexplore.ieee.org/document/9945948/Water quality indexmachine learninghybrid data-mining algorithmscross-validation techniquesNorth Pakistan |
| spellingShingle | Bilal Aslam Ahsen Maqsoom Ali Hassan Cheema Fahim Ullah Abdullah Alharbi Muhammad Imran Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach IEEE Access Water quality index machine learning hybrid data-mining algorithms cross-validation techniques North Pakistan |
| title | Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach |
| title_full | Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach |
| title_fullStr | Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach |
| title_full_unstemmed | Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach |
| title_short | Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach |
| title_sort | water quality management using hybrid machine learning and data mining algorithms an indexing approach |
| topic | Water quality index machine learning hybrid data-mining algorithms cross-validation techniques North Pakistan |
| url | https://ieeexplore.ieee.org/document/9945948/ |
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