Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan
The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climate fluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands of individuals succumb to waterborne diseases. Consequently, the water quality monitorin...
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my.uniten.dspace-338992024-10-14T11:17:24Z Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan Azha S.F. Sidek L.M. Ahmad Z. Zhang J. Basri H. Zawawi M.H. Noh N.M. Ahmed A.N. 56166798400 35070506500 55276521200 47562444500 57065823300 39162217600 57205236493 57214837520 Batu Pahat River Feedforward artificial neural network Mitigation plans SDG Water Energy Security Water quality index Batu Pahat Johor Malaysia West Malaysia Climate change Ecosystems Energy security Feedforward neural networks Learning algorithms Machine learning Network security River pollution Water quality Batu pahat river Feed-forward artificial neural networks Health monitoring Mitigation plans River health SDG Water energy Water energy security Water quality indexes Water quality parameters artificial neural network climate change machine learning river pollution water planning water quality water resource Rivers The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climate fluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands of individuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have been established worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by the intricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robust water quality prediction model. The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. In this particular investigation, machine learning model called Feedforward Artificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River, Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether to include or exclude parameters such as BOD and COD, which are not measured in real time and can be costly to monitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in three different scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the third scenario excluded both BOD and COD. It was suggested that the model has better predictive power between input variables and output variables. Factor contributed to river pollution has been identified and mitigation plan for Batu Pahat river pollution has been proposed. This could provide an effective alternative to compute the pollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems. � 2023 The Author(s) Final 2024-10-14T03:17:24Z 2024-10-14T03:17:24Z 2023 Article 10.1016/j.ecolind.2023.111190 2-s2.0-85175810279 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175810279&doi=10.1016%2fj.ecolind.2023.111190&partnerID=40&md5=9754f7da26194d156484852ca6528b67 https://irepository.uniten.edu.my/handle/123456789/33899 156 111190 All Open Access Gold Open Access Elsevier B.V. Scopus |
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Batu Pahat River Feedforward artificial neural network Mitigation plans SDG Water Energy Security Water quality index Batu Pahat Johor Malaysia West Malaysia Climate change Ecosystems Energy security Feedforward neural networks Learning algorithms Machine learning Network security River pollution Water quality Batu pahat river Feed-forward artificial neural networks Health monitoring Mitigation plans River health SDG Water energy Water energy security Water quality indexes Water quality parameters artificial neural network climate change machine learning river pollution water planning water quality water resource Rivers |
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Batu Pahat River Feedforward artificial neural network Mitigation plans SDG Water Energy Security Water quality index Batu Pahat Johor Malaysia West Malaysia Climate change Ecosystems Energy security Feedforward neural networks Learning algorithms Machine learning Network security River pollution Water quality Batu pahat river Feed-forward artificial neural networks Health monitoring Mitigation plans River health SDG Water energy Water energy security Water quality indexes Water quality parameters artificial neural network climate change machine learning river pollution water planning water quality water resource Rivers Azha S.F. Sidek L.M. Ahmad Z. Zhang J. Basri H. Zawawi M.H. Noh N.M. Ahmed A.N. Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan |
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The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climate fluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands of individuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have been established worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by the intricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robust water quality prediction model. The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. In this particular investigation, machine learning model called Feedforward Artificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River, Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether to include or exclude parameters such as BOD and COD, which are not measured in real time and can be costly to monitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in three different scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the third scenario excluded both BOD and COD. It was suggested that the model has better predictive power between input variables and output variables. Factor contributed to river pollution has been identified and mitigation plan for Batu Pahat river pollution has been proposed. This could provide an effective alternative to compute the pollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems. � 2023 The Author(s) |
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56166798400 |
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56166798400 Azha S.F. Sidek L.M. Ahmad Z. Zhang J. Basri H. Zawawi M.H. Noh N.M. Ahmed A.N. |
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Article |
author |
Azha S.F. Sidek L.M. Ahmad Z. Zhang J. Basri H. Zawawi M.H. Noh N.M. Ahmed A.N. |
author_sort |
Azha S.F. |
title |
Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan |
title_short |
Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan |
title_full |
Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan |
title_fullStr |
Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan |
title_full_unstemmed |
Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan |
title_sort |
enhancing river health monitoring: developing a reliable predictive model and mitigation plan |
publisher |
Elsevier B.V. |
publishDate |
2024 |
_version_ |
1814061030013140992 |
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13.214268 |