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Khudhair, ZS, Zubaidi, SL, Ortega-Martorell, S, Al-Ansari, N, Ethaib, S and Hashim, KS (2022) A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions. Environments, 9 (7). ISSN 2076-3298
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Abstract
Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Civil Engineering & Built Environment Computer Science & Mathematics |
| Publisher: | MDPI |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 04 Jul 2022 07:05 |
| Last Modified: | 05 Jul 2022 10:00 |
| DOI or ID number: | 10.3390/environments9070085 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/17193 |
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