Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios

Abobakr Saeed Abobakr Yahya, Ali Najah Ahmed, Faridah Binti Othman, Rusul Khaleel Ibrahim, Haitham Abdulmohsin Afan, Amr El-Shafie, Chow Ming Fai, Md Shabbir Hossain, Mohammad Ehteram, Ahmed Elshafie

Research output: Contribution to journalArticle

Abstract

Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

Original languageEnglish
Article number1231
JournalWater
Volume11
Issue number6
DOIs
Publication statusPublished - 13 Jun 2019

Fingerprint

Water Quality
Rivers
Catchments
Water quality
Support vector machines
water quality
river
catchment
scenario
water
rivers
prediction
dissolved oxygen
Dissolved oxygen
Oxygen
water quality analysis
biochemical oxygen demand
pollution control
chemical oxygen demand
Biological Oxygen Demand Analysis

Keywords

  • Dissolved oxygen
  • Support vector machine
  • Water quality

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

Yahya, A. S. A., Ahmed, A. N., Othman, F. B., Ibrahim, R. K., Afan, H. A., El-Shafie, A., ... Elshafie, A. (2019). Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios. Water, 11(6), [1231]. https://doi.org/10.3390/w11061231
Yahya, Abobakr Saeed Abobakr ; Ahmed, Ali Najah ; Othman, Faridah Binti ; Ibrahim, Rusul Khaleel ; Afan, Haitham Abdulmohsin ; El-Shafie, Amr ; Fai, Chow Ming ; Hossain, Md Shabbir ; Ehteram, Mohammad ; Elshafie, Ahmed. / Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios. In: Water. 2019 ; Vol. 11, No. 6.
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Yahya, ASA, Ahmed, AN, Othman, FB, Ibrahim, RK, Afan, HA, El-Shafie, A, Fai, CM, Hossain, MS, Ehteram, M & Elshafie, A 2019, 'Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios', Water, vol. 11, no. 6, 1231. https://doi.org/10.3390/w11061231

Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios. / Yahya, Abobakr Saeed Abobakr; Ahmed, Ali Najah; Othman, Faridah Binti; Ibrahim, Rusul Khaleel; Afan, Haitham Abdulmohsin; El-Shafie, Amr; Fai, Chow Ming; Hossain, Md Shabbir; Ehteram, Mohammad; Elshafie, Ahmed.

In: Water, Vol. 11, No. 6, 1231, 13.06.2019.

Research output: Contribution to journalArticle

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AU - Yahya, Abobakr Saeed Abobakr

AU - Ahmed, Ali Najah

AU - Othman, Faridah Binti

AU - Ibrahim, Rusul Khaleel

AU - Afan, Haitham Abdulmohsin

AU - El-Shafie, Amr

AU - Fai, Chow Ming

AU - Hossain, Md Shabbir

AU - Ehteram, Mohammad

AU - Elshafie, Ahmed

PY - 2019/6/13

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N2 - Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

AB - Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

KW - Dissolved oxygen

KW - Support vector machine

KW - Water quality

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