River water quality monitoring using machine learning with multiple possible in-situ scenarios
Water quality is influenced by a wide range of factors, but it is expensive and technically difficult to take into account every factor, which leaves out quality variations. The assessment process is made more difficult by the need for different evaluation indicators for various water uses. Furtherm...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Environmental and Sustainability Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972725000418 |
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| author | Dani Irwan Saerahany Legori Ibrahim Sarmad Dashti Latif Chris Aaron Winston Ali Najah Ahmed Mohsen Sherif Amr H. El-Shafie Ahmed El-Shafie |
| author_facet | Dani Irwan Saerahany Legori Ibrahim Sarmad Dashti Latif Chris Aaron Winston Ali Najah Ahmed Mohsen Sherif Amr H. El-Shafie Ahmed El-Shafie |
| author_sort | Dani Irwan |
| collection | DOAJ |
| description | Water quality is influenced by a wide range of factors, but it is expensive and technically difficult to take into account every factor, which leaves out quality variations. The assessment process is made more difficult by the need for different evaluation indicators for various water uses. Furthermore, many water quality factors have complex nonlinear relationships that are difficult for these methods to handle. On the other hand, because machine learning can quickly identify underlying principles and handle complex data with efficiency, it offers a promising approach. The gap involves addressing complex relationship and environmental factors when predicting water quality in rivers. The purpose of this study is to evaluate the feasibility of estimating the Gombak River's Water Quality Index (WQI) using machine learning, and to identify appropriate models based on statistical performance metrics. The study looks into the possibility of estimating WQI solely using dissolved oxygen (DO) and pH as predictors because the chemical parameters in the current Malaysian WQI calculation method takes some time to compute. This research provides insight into the accuracy, precision, and general performance of these models in predicting water quality by looking at the residuals of various scenarios and evaluating performance metrics across different machine learning models. This study provides insights into the potential of machine learning for improving water quality assessment and management practices. Future studies should concentrate on resolving these issues and investigating other elements, such as environmental variables, land use patterns, and human activity, that may affect the forecast of water quality. |
| format | Article |
| id | doaj-art-aa367cf243a84e07ae4ed9a59ec924f7 |
| institution | Kabale University |
| issn | 2665-9727 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj-art-aa367cf243a84e07ae4ed9a59ec924f72025-02-07T04:48:18ZengElsevierEnvironmental and Sustainability Indicators2665-97272025-06-0126100620River water quality monitoring using machine learning with multiple possible in-situ scenariosDani Irwan0Saerahany Legori Ibrahim1Sarmad Dashti Latif2Chris Aaron Winston3Ali Najah Ahmed4Mohsen Sherif5Amr H. El-Shafie6Ahmed El-Shafie7Department of Civil Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, P.O Box 10, 50728, Kuala Lumpur, MalaysiaDepartment of Civil Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, P.O Box 10, 50728, Kuala Lumpur, MalaysiaCivil Engineering Department, College of Engineering, Komar University of Science and Technology, Sulaymaniyah, Kurdistan Region, 46001, IraqDepartment of Civil Engineering, Faculty of Engineering, University Malaya, MalaysiaResearch Centre For Human-Machine Collaboration (HUMAC), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; Department of Engineering, School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; Corresponding author. Research Centre For Human-Machine Collaboration (HUMAC), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia.National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab EmiratesCivil Engineering Department, Al-Gazeera High Institute for Engineering and Technology, Al Mokatam, Cairo, EgyptNational Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates; Corresponding author.Water quality is influenced by a wide range of factors, but it is expensive and technically difficult to take into account every factor, which leaves out quality variations. The assessment process is made more difficult by the need for different evaluation indicators for various water uses. Furthermore, many water quality factors have complex nonlinear relationships that are difficult for these methods to handle. On the other hand, because machine learning can quickly identify underlying principles and handle complex data with efficiency, it offers a promising approach. The gap involves addressing complex relationship and environmental factors when predicting water quality in rivers. The purpose of this study is to evaluate the feasibility of estimating the Gombak River's Water Quality Index (WQI) using machine learning, and to identify appropriate models based on statistical performance metrics. The study looks into the possibility of estimating WQI solely using dissolved oxygen (DO) and pH as predictors because the chemical parameters in the current Malaysian WQI calculation method takes some time to compute. This research provides insight into the accuracy, precision, and general performance of these models in predicting water quality by looking at the residuals of various scenarios and evaluating performance metrics across different machine learning models. This study provides insights into the potential of machine learning for improving water quality assessment and management practices. Future studies should concentrate on resolving these issues and investigating other elements, such as environmental variables, land use patterns, and human activity, that may affect the forecast of water quality.http://www.sciencedirect.com/science/article/pii/S2665972725000418Machine learningWater qualityNeural networkMinimal input |
| spellingShingle | Dani Irwan Saerahany Legori Ibrahim Sarmad Dashti Latif Chris Aaron Winston Ali Najah Ahmed Mohsen Sherif Amr H. El-Shafie Ahmed El-Shafie River water quality monitoring using machine learning with multiple possible in-situ scenarios Environmental and Sustainability Indicators Machine learning Water quality Neural network Minimal input |
| title | River water quality monitoring using machine learning with multiple possible in-situ scenarios |
| title_full | River water quality monitoring using machine learning with multiple possible in-situ scenarios |
| title_fullStr | River water quality monitoring using machine learning with multiple possible in-situ scenarios |
| title_full_unstemmed | River water quality monitoring using machine learning with multiple possible in-situ scenarios |
| title_short | River water quality monitoring using machine learning with multiple possible in-situ scenarios |
| title_sort | river water quality monitoring using machine learning with multiple possible in situ scenarios |
| topic | Machine learning Water quality Neural network Minimal input |
| url | http://www.sciencedirect.com/science/article/pii/S2665972725000418 |
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