Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios
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 suppor...
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my.uniten.dspace-130022020-07-07T01:49:56Z 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. 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. 2020-02-03T03:28:26Z 2020-02-03T03:28:26Z 2019 Article 10.3390/w11061231 en |
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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. |
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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_facet |
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. |
| 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 |
| publishDate |
2020 |
| _version_ |
1672614198036660224 |
| score |
13.222552 |