A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality's Parameters: Current Trends and Future Directions



Khudhair, Zahraa S, Zubaidi, Salah L, Ortega-Martorell, Sandra ORCID: 0000-0001-9927-3209, Al-Ansari, Nadhir, Ethaib, Saleem and Hashim, Khalid
(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). p. 85.

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Abstract

<jats:p>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.</jats:p>

Item Type: Article
Uncontrolled Keywords: water quality parameters, hybrid model, metaheuristic algorithms, machine learning
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 15 May 2023 13:15
Last Modified: 15 May 2023 13:16
DOI: 10.3390/environments9070085
Open Access URL: https://doi.org/10.3390/environments9070085
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3170381