Efficient river water quality index prediction considering minimal number of inputs variables
Water Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand�(COD), biochemical oxygen demand�(BOD), dissolved oxygen�(DO), suspended s...
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my.uniten.dspace-257082023-05-29T16:13:06Z Efficient river water quality index prediction considering minimal number of inputs variables Othman F. Alaaeldin M.E. Seyam M. Ahmed A.N. Teo F.Y. Ming Fai C. Afan H.A. Sherif M. Sefelnasr A. El-Shafie A. 36630785100 57217306176 56182818000 57214837520 35249518400 57214146115 56436626600 7005414714 6505592467 16068189400 Water Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand�(COD), biochemical oxygen demand�(BOD), dissolved oxygen�(DO), suspended solids�(SS), -potential for hydrogen (pH), and ammoniacal nitrogen�(AN). In fact, understanding the inter-relationships between these variables and WQI can improve predicting the WQI for better water resources management. The aim of this study is to create an input approach using ANNs (Artificial Neural Networks) to compute the WQI from input parameters instead of using the indices of the parameters when one of the parameters is absent. The data are collected from the nine water quality monitoring stations at the Klang River basin, Malaysia. In addition, comprehensive sensitivity analysis has been carried out to identify the most influential input parameters. The model is based on the frequency distribution of the significant factors showed exceptional ability to replicate the WQI and attained very high correlation (98.78%). Furthermore, the sensitivity analysis showed that the most influential parameter that affects WQI is DO, while pH is the least one. Additionally, the performance of models shows that the missing DO values caused deterioration in the accuracy. � 2020, � 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Final 2023-05-29T08:13:06Z 2023-05-29T08:13:06Z 2020 Article 10.1080/19942060.2020.1760942 2-s2.0-85087044977 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087044977&doi=10.1080%2f19942060.2020.1760942&partnerID=40&md5=e0847b8394a01f3758bc3021bd3d685f https://irepository.uniten.edu.my/handle/123456789/25708 14 1 751 763 All Open Access, Gold Taylor and Francis Ltd. Scopus |
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Water Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand�(COD), biochemical oxygen demand�(BOD), dissolved oxygen�(DO), suspended solids�(SS), -potential for hydrogen (pH), and ammoniacal nitrogen�(AN). In fact, understanding the inter-relationships between these variables and WQI can improve predicting the WQI for better water resources management. The aim of this study is to create an input approach using ANNs (Artificial Neural Networks) to compute the WQI from input parameters instead of using the indices of the parameters when one of the parameters is absent. The data are collected from the nine water quality monitoring stations at the Klang River basin, Malaysia. In addition, comprehensive sensitivity analysis has been carried out to identify the most influential input parameters. The model is based on the frequency distribution of the significant factors showed exceptional ability to replicate the WQI and attained very high correlation (98.78%). Furthermore, the sensitivity analysis showed that the most influential parameter that affects WQI is DO, while pH is the least one. Additionally, the performance of models shows that the missing DO values caused deterioration in the accuracy. � 2020, � 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. |
| author2 |
36630785100 |
| author_facet |
36630785100 Othman F. Alaaeldin M.E. Seyam M. Ahmed A.N. Teo F.Y. Ming Fai C. Afan H.A. Sherif M. Sefelnasr A. El-Shafie A. |
| format |
Article |
| author |
Othman F. Alaaeldin M.E. Seyam M. Ahmed A.N. Teo F.Y. Ming Fai C. Afan H.A. Sherif M. Sefelnasr A. El-Shafie A. |
| spellingShingle |
Othman F. Alaaeldin M.E. Seyam M. Ahmed A.N. Teo F.Y. Ming Fai C. Afan H.A. Sherif M. Sefelnasr A. El-Shafie A. Efficient river water quality index prediction considering minimal number of inputs variables |
| author_sort |
Othman F. |
| title |
Efficient river water quality index prediction considering minimal number of inputs variables |
| title_short |
Efficient river water quality index prediction considering minimal number of inputs variables |
| title_full |
Efficient river water quality index prediction considering minimal number of inputs variables |
| title_fullStr |
Efficient river water quality index prediction considering minimal number of inputs variables |
| title_full_unstemmed |
Efficient river water quality index prediction considering minimal number of inputs variables |
| title_sort |
efficient river water quality index prediction considering minimal number of inputs variables |
| publisher |
Taylor and Francis Ltd. |
| publishDate |
2023 |
| _version_ |
1806424103863713792 |
| score |
13.222552 |