WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
Water presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we devel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/1/262 |
| _version_ | 1797359127499898880 |
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| author | Firna Firdiani Satria Mandala Adiwijaya Abdul Hanan Abdullah |
| author_facet | Firna Firdiani Satria Mandala Adiwijaya Abdul Hanan Abdullah |
| author_sort | Firna Firdiani |
| collection | DOAJ |
| description | Water presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we developed a water quality prediction system (WaQuPs), a sophisticated solution tackling the intricacies of water quality assessment. WaQuPs employs advanced machine learning, including an ensemble learning model, categorizing water quality into nuanced levels: potable, lightly polluted, moderately polluted, and heavily polluted. To ensure rapid and precise dissemination of information, WaQuPs integrates an Internet of Things (IoT)-based communication protocol for the efficient delivery of detected water quality results. In its development, we utilized advanced techniques, such as random oversampling (ROS) for dataset balance. We used a correlation coefficient to select relevant features for the ensemble learning algorithm based on the Random Forest algorithm. Further enhancements were made through hyperparameter tuning to improve the prediction accuracy. WaQuPs exhibited impressive metrics, achieving an accuracy of 83%, precision of 82%, recall of 83%, and an F1-score of 82%. Comparative analysis revealed that WaQuPs with the Random Forest model outperformed both the XGBoost and CatBoost models, confirming its superiority in predicting water quality. |
| first_indexed | 2024-03-08T15:11:31Z |
| format | Article |
| id | doaj.art-3ca7f6cdb4204c3ab6b5ed0340e1cdd7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:11:31Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3ca7f6cdb4204c3ab6b5ed0340e1cdd72024-01-10T14:51:31ZengMDPI AGApplied Sciences2076-34172023-12-0114126210.3390/app14010262WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality PredictionFirna Firdiani0Satria Mandala1Adiwijaya2Abdul Hanan Abdullah3Human Centric Engineering & School of Computing, Telkom University, Jl. Telekomunikasi No. 1, Bandung 40257, West Java, IndonesiaHuman Centric Engineering & School of Computing, Telkom University, Jl. Telekomunikasi No. 1, Bandung 40257, West Java, IndonesiaHuman Centric Engineering & School of Computing, Telkom University, Jl. Telekomunikasi No. 1, Bandung 40257, West Java, IndonesiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaWater presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we developed a water quality prediction system (WaQuPs), a sophisticated solution tackling the intricacies of water quality assessment. WaQuPs employs advanced machine learning, including an ensemble learning model, categorizing water quality into nuanced levels: potable, lightly polluted, moderately polluted, and heavily polluted. To ensure rapid and precise dissemination of information, WaQuPs integrates an Internet of Things (IoT)-based communication protocol for the efficient delivery of detected water quality results. In its development, we utilized advanced techniques, such as random oversampling (ROS) for dataset balance. We used a correlation coefficient to select relevant features for the ensemble learning algorithm based on the Random Forest algorithm. Further enhancements were made through hyperparameter tuning to improve the prediction accuracy. WaQuPs exhibited impressive metrics, achieving an accuracy of 83%, precision of 82%, recall of 83%, and an F1-score of 82%. Comparative analysis revealed that WaQuPs with the Random Forest model outperformed both the XGBoost and CatBoost models, confirming its superiority in predicting water quality.https://www.mdpi.com/2076-3417/14/1/262Internet of Thingsmachine learningwater quality predictionRandom Forestrandom oversampling |
| spellingShingle | Firna Firdiani Satria Mandala Adiwijaya Abdul Hanan Abdullah WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction Applied Sciences Internet of Things machine learning water quality prediction Random Forest random oversampling |
| title | WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction |
| title_full | WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction |
| title_fullStr | WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction |
| title_full_unstemmed | WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction |
| title_short | WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction |
| title_sort | waqups a ros integrated ensemble learning model for precise water quality prediction |
| topic | Internet of Things machine learning water quality prediction Random Forest random oversampling |
| url | https://www.mdpi.com/2076-3417/14/1/262 |
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