Hybrid neural network ensembles for predicting water quality in municipal treatment systems

I. A. Mahmoud; M. Alsubih; M. Alkahtani; S. Sa’id; U. U. Aliyu

doi:10.1007/s13762-026-07052-0

February 2, 2026International Journal of Environmental Science and Technology

Hybrid neural network ensembles for predicting water quality in municipal treatment systems

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Key Points

Predictive modeling achieves a significant increase in accuracy for water quality assessment.
The hybrid neural network ensemble model improved overall prediction quality by 25% compared to single models.
Observational analysis across various municipal treatment system datasets demonstrated robust predictions under diverse conditions.
These findings highlight the potential of advanced algorithms in optimizing treatment system efficiency.

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Implication

Observational analysis reveals improved water quality predictions using hybrid neural network ensembles, suggesting enhanced municipal treatment processes.

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Mahmoud et al. (2026) studied this question.

synapsesocial.com/papers/69a765a4badf0bb9e87d9d7b https://doi.org/10.1007/s13762-026-07052-0

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Hybrid neural network ensembles for predicting water quality in municipal treatment systems

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Hybrid neural network ensembles for predicting water quality in municipal treatment systems

Key Points

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