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arXiv · 2602.08050

Interpretable Fuzzy Systems For Forward Osmosis Desalination

Qusai Khaled·Uzay Kaymak·Laura Genga
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Abstract

Preserving interpretability in fuzzy rule-based systems (FRBS) is vital for water treatment, where decisions impact public health. While structural interpretability has been addressed using multi-objective algorithms, semantic interpretability often suffers due to fuzzy sets with low distinguishability. We propose a human-in-the-loop approach for developing interpretable FRBS to predict forward osmosis desalination productivity. Our method integrates expert-driven grid partitioning for distinguishable membership functions, domain-guided feature engineering to reduce redundancy, and rule pruning based on firing strength. This approach achieved comparable predictive performance to cluster-based FRBS while maintaining semantic interpretability and meeting structural complexity constraints, providing an explainable solution for water treatment applications.

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Qusai Khaled, Uzay Kaymak, Laura Genga. 2026-02-08. Interpretable Fuzzy Systems For Forward Osmosis Desalination. https://doi.org/10.1109/fuzz62266.2025.11152221

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