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

Abobakr Yahya, Abobakr Saeed and Ahmed, Ali Najah and Othman, Faridah and Ibrahim, Rusul Khaleel and Afan, Haitham Abdulmohsin and El-Shafie, Amr and Fai, Chow Ming and Hossain, Md Shabbir and Ehteram, Mohammad and El-Shafie, Ahmed (2019) Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios. Water, 11 (6). p. 1231. ISSN 2073-4441, DOI https://doi.org/10.3390/w11061231.

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Official URL: https://doi.org/10.3390/w11061231


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. © 2019 by the authors.

Item Type: Article
Funders: Innovation & Research Management Center (iRMC), Universiti Tenaga Nasional, Malaysia, Bold 2025 grant coded RJO 10436494 and J510050822, University of Malaya grant coded UMRG RP025A-18SUS
Uncontrolled Keywords: support vector machine; water quality; dissolved oxygen
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 27 Nov 2019 09:15
Last Modified: 27 Nov 2019 09:15
URI: http://eprints.um.edu.my/id/eprint/23133

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