Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios
Ammonia; Biochemical oxygen demand; Catchments; Dissolved oxygen; Forecasting; Reservoirs (water); River pollution; Rivers; Runoff; Support vector machines; Water pollution control; Water quality; 10-fold cross-validation; Biochemical oxygen demands (BOD); Correlation coefficient; Environmental stan...
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my.uniten.dspace-246412023-05-29T15:25:24Z Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios Yahya A.S.A. Ahmed A.N. Othman F.B. Ibrahim R.K. Afan H.A. El-Shafie A. Fai C.M. Hossain M.S. Ehteram M. Elshafie A. 57206338836 57214837520 36630785100 57188832586 56436626600 57207789882 57214146115 55579596900 57113510800 16068189400 Ammonia; Biochemical oxygen demand; Catchments; Dissolved oxygen; Forecasting; Reservoirs (water); River pollution; Rivers; Runoff; Support vector machines; Water pollution control; Water quality; 10-fold cross-validation; Biochemical oxygen demands (BOD); Correlation coefficient; Environmental standards; Support vector machine models; Water quality parameters; Water quality predictions; Water resources management; Quality control; accuracy assessment; catchment; error analysis; model validation; numerical model; prediction; support vector machine; water quality; Langat Basin; Malaysia; West Malaysia 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. Final 2023-05-29T07:25:24Z 2023-05-29T07:25:24Z 2019 Article 10.3390/w11061231 2-s2.0-85068884314 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068884314&doi=10.3390%2fw11061231&partnerID=40&md5=139cd3784f887c246833d61b86709320 https://irepository.uniten.edu.my/handle/123456789/24641 11 6 1231 All Open Access, Gold, Green MDPI AG Scopus |
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| description |
Ammonia; Biochemical oxygen demand; Catchments; Dissolved oxygen; Forecasting; Reservoirs (water); River pollution; Rivers; Runoff; Support vector machines; Water pollution control; Water quality; 10-fold cross-validation; Biochemical oxygen demands (BOD); Correlation coefficient; Environmental standards; Support vector machine models; Water quality parameters; Water quality predictions; Water resources management; Quality control; accuracy assessment; catchment; error analysis; model validation; numerical model; prediction; support vector machine; water quality; Langat Basin; Malaysia; West Malaysia |
| author2 |
57206338836 |
| author_facet |
57206338836 Yahya A.S.A. Ahmed A.N. Othman F.B. Ibrahim R.K. Afan H.A. El-Shafie A. Fai C.M. Hossain M.S. Ehteram M. Elshafie A. |
| format |
Article |
| author |
Yahya A.S.A. Ahmed A.N. Othman F.B. Ibrahim R.K. Afan H.A. El-Shafie A. Fai C.M. Hossain M.S. Ehteram M. Elshafie A. |
| spellingShingle |
Yahya A.S.A. Ahmed A.N. Othman F.B. Ibrahim R.K. Afan H.A. El-Shafie A. Fai C.M. Hossain M.S. Ehteram M. Elshafie A. Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios |
| author_sort |
Yahya A.S.A. |
| title |
Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios |
| title_short |
Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios |
| title_full |
Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios |
| title_fullStr |
Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios |
| title_full_unstemmed |
Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios |
| title_sort |
water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806427785375252480 |
| score |
13.222552 |