A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq
Salinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal mode...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2022.2150121 |
| _version_ | 1827837809231134720 |
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| author | Zahraa S. Khudhair Salah L. Zubaidi Hussein Al-Bugharbee Nadhir Al-Ansari Hussein Mohammed Ridha |
| author_facet | Zahraa S. Khudhair Salah L. Zubaidi Hussein Al-Bugharbee Nadhir Al-Ansari Hussein Mohammed Ridha |
| author_sort | Zahraa S. Khudhair |
| collection | DOAJ |
| description | Salinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal modelling that can capture the salinity behaviour is becoming a common trend in recent water quality research. This study applies novel combined techniques, including data pre-processing and artificial neural network (ANN) optimised with constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) to forecast monthly salinity data. Historical monthly total dissolved solids (TDS) and electrical conductivity (EC) data of the Euphrates River at Al-Musayyab, Babylon, and climatic factors from 2010 to 2019 were used to build and validate the methodology. Additionally, for more validation, the CPSOCGSA-ANN was compared with the slime mould algorithm (SMA-ANN), particle swarm optimisation (PSO-ANN) and multi-verse optimiser (MVO-ANN). The results reveal that the pre-processing data approaches improved data quality and selected the best predictors’ scenario. The CPSOCGSA-ANN algorithm is the best based on several statistical criteria. The proposed methodology accurately simulated the TDS and EC time series based on R2 = 0.99 and 0.97, respectively, and SI = 0.003 for both parameters. |
| first_indexed | 2024-03-12T06:52:20Z |
| format | Article |
| id | doaj.art-c9a3193459294e9280231c6beaccf7f1 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T06:52:20Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-c9a3193459294e9280231c6beaccf7f12023-09-03T00:11:46ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2022.2150121A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, IraqZahraa S. Khudhair0Salah L. Zubaidi1Hussein Al-Bugharbee2Nadhir Al-Ansari3Hussein Mohammed Ridha4Department of Civil Engineering, Wasit University, Wasit, 52001, IraqDepartment of Civil Engineering, Wasit University, Wasit, 52001, IraqDepartment of Mechanical Engineering, Wasit University, Wasit, 52001, IraqDepartment of Civil Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, SwedenDepartment of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaSalinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal modelling that can capture the salinity behaviour is becoming a common trend in recent water quality research. This study applies novel combined techniques, including data pre-processing and artificial neural network (ANN) optimised with constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) to forecast monthly salinity data. Historical monthly total dissolved solids (TDS) and electrical conductivity (EC) data of the Euphrates River at Al-Musayyab, Babylon, and climatic factors from 2010 to 2019 were used to build and validate the methodology. Additionally, for more validation, the CPSOCGSA-ANN was compared with the slime mould algorithm (SMA-ANN), particle swarm optimisation (PSO-ANN) and multi-verse optimiser (MVO-ANN). The results reveal that the pre-processing data approaches improved data quality and selected the best predictors’ scenario. The CPSOCGSA-ANN algorithm is the best based on several statistical criteria. The proposed methodology accurately simulated the TDS and EC time series based on R2 = 0.99 and 0.97, respectively, and SI = 0.003 for both parameters.https://www.tandfonline.com/doi/10.1080/23311916.2022.2150121ANNsalinity forecast modelmetaheuristic algorithmEuphrates river |
| spellingShingle | Zahraa S. Khudhair Salah L. Zubaidi Hussein Al-Bugharbee Nadhir Al-Ansari Hussein Mohammed Ridha A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq Cogent Engineering ANN salinity forecast model metaheuristic algorithm Euphrates river |
| title | A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq |
| title_full | A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq |
| title_fullStr | A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq |
| title_full_unstemmed | A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq |
| title_short | A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq |
| title_sort | cpsocgsa tuned neural processor for forecasting river water salinity euphrates river iraq |
| topic | ANN salinity forecast model metaheuristic algorithm Euphrates river |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2022.2150121 |
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