Towards a time and cost effective approach to water quality index class prediction

Jun Yung Ho, Haitham Abdulmohsin Afan, Amr H. El-Shafie, Suhana Binti Koting, Nuruol Syuhadaa Mohd, Wan Zurina Binti Jaafar, Hin Lai Sai, Marlinda Abdul Malek, Ali Najah Ahmed, Wan Hanna Melini Wan Mohtar, Amin Elshorbagy, Ahmed El-Shafie

Research output: Contribution to journalArticle

Abstract

The development of water quality prediction models is an important step towards better water quality management of rivers. The traditional method for computing WQI is always associated with errors due to the protracted analysis of the water quality parameters in addition to the great effort and time involved in gathering and analyzing water samples. In addition, the cost of identifying the magnitude of some of the parameters through experimental testing is very high. The water quality of rivers in Malaysia is ranked into five classes based on water quality index (WQI). WQI is function of six water quality parameters: ammoniac nitrogen (NH3-N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). In this research, the decision tree machine learning technique is used to predict the WQI for the Klang River and its classification within a specific water quality class. Klang River is one of the most polluted rivers in Malaysia. Modeling experiments are designed to test the prediction and classification accuracy of the model based on various scenarios composed of different water quality parameters. Results show that the proposed prediction model has a promising potential to predict the class of the WQI. Moreover, the proposed model offers a more efficient process and cost-effective approach for the computation and prediction of WQI.

Original languageEnglish
Pages (from-to)148-165
Number of pages18
JournalJournal of Hydrology
Volume575
DOIs
Publication statusPublished - 01 Aug 2019

Fingerprint

water quality
prediction
cost
river
index
biochemical oxygen demand
chemical oxygen demand
dissolved oxygen
parameter
nitrogen
modeling

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Cite this

Ho, J. Y., Afan, H. A., El-Shafie, A. H., Koting, S. B., Mohd, N. S., Jaafar, W. Z. B., ... El-Shafie, A. (2019). Towards a time and cost effective approach to water quality index class prediction. Journal of Hydrology, 575, 148-165. https://doi.org/10.1016/j.jhydrol.2019.05.016
Ho, Jun Yung ; Afan, Haitham Abdulmohsin ; El-Shafie, Amr H. ; Koting, Suhana Binti ; Mohd, Nuruol Syuhadaa ; Jaafar, Wan Zurina Binti ; Lai Sai, Hin ; Abdul Malek, Marlinda ; Ahmed, Ali Najah ; Mohtar, Wan Hanna Melini Wan ; Elshorbagy, Amin ; El-Shafie, Ahmed. / Towards a time and cost effective approach to water quality index class prediction. In: Journal of Hydrology. 2019 ; Vol. 575. pp. 148-165.
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abstract = "The development of water quality prediction models is an important step towards better water quality management of rivers. The traditional method for computing WQI is always associated with errors due to the protracted analysis of the water quality parameters in addition to the great effort and time involved in gathering and analyzing water samples. In addition, the cost of identifying the magnitude of some of the parameters through experimental testing is very high. The water quality of rivers in Malaysia is ranked into five classes based on water quality index (WQI). WQI is function of six water quality parameters: ammoniac nitrogen (NH3-N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). In this research, the decision tree machine learning technique is used to predict the WQI for the Klang River and its classification within a specific water quality class. Klang River is one of the most polluted rivers in Malaysia. Modeling experiments are designed to test the prediction and classification accuracy of the model based on various scenarios composed of different water quality parameters. Results show that the proposed prediction model has a promising potential to predict the class of the WQI. Moreover, the proposed model offers a more efficient process and cost-effective approach for the computation and prediction of WQI.",
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Ho, JY, Afan, HA, El-Shafie, AH, Koting, SB, Mohd, NS, Jaafar, WZB, Lai Sai, H, Abdul Malek, M, Ahmed, AN, Mohtar, WHMW, Elshorbagy, A & El-Shafie, A 2019, 'Towards a time and cost effective approach to water quality index class prediction', Journal of Hydrology, vol. 575, pp. 148-165. https://doi.org/10.1016/j.jhydrol.2019.05.016

Towards a time and cost effective approach to water quality index class prediction. / Ho, Jun Yung; Afan, Haitham Abdulmohsin; El-Shafie, Amr H.; Koting, Suhana Binti; Mohd, Nuruol Syuhadaa; Jaafar, Wan Zurina Binti; Lai Sai, Hin; Abdul Malek, Marlinda; Ahmed, Ali Najah; Mohtar, Wan Hanna Melini Wan; Elshorbagy, Amin; El-Shafie, Ahmed.

In: Journal of Hydrology, Vol. 575, 01.08.2019, p. 148-165.

Research output: Contribution to journalArticle

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AU - Ho, Jun Yung

AU - Afan, Haitham Abdulmohsin

AU - El-Shafie, Amr H.

AU - Koting, Suhana Binti

AU - Mohd, Nuruol Syuhadaa

AU - Jaafar, Wan Zurina Binti

AU - Lai Sai, Hin

AU - Abdul Malek, Marlinda

AU - Ahmed, Ali Najah

AU - Mohtar, Wan Hanna Melini Wan

AU - Elshorbagy, Amin

AU - El-Shafie, Ahmed

PY - 2019/8/1

Y1 - 2019/8/1

N2 - The development of water quality prediction models is an important step towards better water quality management of rivers. The traditional method for computing WQI is always associated with errors due to the protracted analysis of the water quality parameters in addition to the great effort and time involved in gathering and analyzing water samples. In addition, the cost of identifying the magnitude of some of the parameters through experimental testing is very high. The water quality of rivers in Malaysia is ranked into five classes based on water quality index (WQI). WQI is function of six water quality parameters: ammoniac nitrogen (NH3-N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). In this research, the decision tree machine learning technique is used to predict the WQI for the Klang River and its classification within a specific water quality class. Klang River is one of the most polluted rivers in Malaysia. Modeling experiments are designed to test the prediction and classification accuracy of the model based on various scenarios composed of different water quality parameters. Results show that the proposed prediction model has a promising potential to predict the class of the WQI. Moreover, the proposed model offers a more efficient process and cost-effective approach for the computation and prediction of WQI.

AB - The development of water quality prediction models is an important step towards better water quality management of rivers. The traditional method for computing WQI is always associated with errors due to the protracted analysis of the water quality parameters in addition to the great effort and time involved in gathering and analyzing water samples. In addition, the cost of identifying the magnitude of some of the parameters through experimental testing is very high. The water quality of rivers in Malaysia is ranked into five classes based on water quality index (WQI). WQI is function of six water quality parameters: ammoniac nitrogen (NH3-N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). In this research, the decision tree machine learning technique is used to predict the WQI for the Klang River and its classification within a specific water quality class. Klang River is one of the most polluted rivers in Malaysia. Modeling experiments are designed to test the prediction and classification accuracy of the model based on various scenarios composed of different water quality parameters. Results show that the proposed prediction model has a promising potential to predict the class of the WQI. Moreover, the proposed model offers a more efficient process and cost-effective approach for the computation and prediction of WQI.

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