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

1 Citation (Scopus)

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 - 1 Aug 2019

Fingerprint

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

Keywords

  • Decision tree model
  • Prediction model
  • River water quality
  • Water quality index

ASJC Scopus subject areas

  • 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

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; Malek, Marlinda Abdul; Ahmed, Ali Najah; Wan Mohtar, Wan Hanna Melini; Elshorbagy, Amin; El-Shafie, Ahmed.

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

Research output: Contribution to journalArticle

Ho, JY, Afan, HA, El-Shafie, AH, Koting, SB, Mohd, NS, Jaafar, WZB, Lai Sai, H, Malek, MA, Ahmed, AN, Wan Mohtar, WHM, 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
Ho, Jun Yung ; Afan, Haitham Abdulmohsin ; El-Shafie, Amr H. ; Koting, Suhana Binti ; Mohd, Nuruol Syuhadaa ; Jaafar, Wan Zurina Binti ; Lai Sai, Hin ; Malek, Marlinda Abdul ; Ahmed, Ali Najah ; Wan Mohtar, Wan Hanna Melini ; 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.
@article{e0d12f84357b4d69a4e2d19dd57691a4,
title = "Towards a time and cost effective approach to water quality index class prediction",
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.",
keywords = "Decision tree model, Prediction model, River water quality, Water quality index",
author = "Ho, {Jun Yung} and Afan, {Haitham Abdulmohsin} and El-Shafie, {Amr H.} and Koting, {Suhana Binti} and Mohd, {Nuruol Syuhadaa} and Jaafar, {Wan Zurina Binti} and {Lai Sai}, Hin and Malek, {Marlinda Abdul} and Ahmed, {Ali Najah} and {Wan Mohtar}, {Wan Hanna Melini} and Amin Elshorbagy and Ahmed El-Shafie",
year = "2019",
month = "8",
day = "1",
doi = "10.1016/j.jhydrol.2019.05.016",
language = "English",
volume = "575",
pages = "148--165",
journal = "Journal of Hydrology",
issn = "0022-1694",
publisher = "Elsevier",

}

TY - JOUR

T1 - Towards a time and cost effective approach to water quality index class prediction

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 - Malek, Marlinda Abdul

AU - Ahmed, Ali Najah

AU - Wan Mohtar, Wan Hanna Melini

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.

KW - Decision tree model

KW - Prediction model

KW - River water quality

KW - Water quality index

UR - http://www.scopus.com/inward/record.url?scp=85066089349&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066089349&partnerID=8YFLogxK

U2 - 10.1016/j.jhydrol.2019.05.016

DO - 10.1016/j.jhydrol.2019.05.016

M3 - Article

AN - SCOPUS:85066089349

VL - 575

SP - 148

EP - 165

JO - Journal of Hydrology

JF - Journal of Hydrology

SN - 0022-1694

ER -