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

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...

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Bibliographic Details
Published in:Environments (Basel, Switzerland) Switzerland), 2022-07, Vol.9 (7), p.85
Main Authors: Khudhair, Zahraa S., Zubaidi, Salah L., Ortega-Martorell, Sandra, Al-Ansari, Nadhir, Ethaib, Saleem, Hashim, Khalid
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Chemical oxygen demand
Data processing
Dissolved oxygen
Forecasting
Freshwater resources
Geoteknik
hybrid model
Internet of Things
Machine learning
Mathematical models
metaheuristic algorithms
Model accuracy
Modelling
Neural networks
Parameters
Prediction models
Rain
Rivers
Soft computing
Soil Mechanics
Support vector machines
Time series
Water quality
water quality parameters
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Summary: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.
ISSN:2076-3298
2076-3298
DOI:10.3390/environments9070085