Interpretable Fuzzy Systems For Forward Osmosis Desalination
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912888508121088 |
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| author | Khaled, Qusai Kaymak, Uzay Genga, Laura |
| author_facet | Khaled, Qusai Kaymak, Uzay Genga, Laura |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_08050 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Interpretable Fuzzy Systems For Forward Osmosis Desalination Khaled, Qusai Kaymak, Uzay Genga, Laura Machine Learning I.2.6 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. |
| title | Interpretable Fuzzy Systems For Forward Osmosis Desalination |
| topic | Machine Learning I.2.6 |
| url | https://arxiv.org/abs/2602.08050 |