# Interpretable Fuzzy Systems For Forward Osmosis Desalination

arXiv: https://arxiv.org/abs/2602.08050 · HTML: https://mlsanity.com/paper/2602.08050/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems](https://mlsanity.com/paper/2209.07175/) (2022)

## Built on by (most cited first, 1 of 1)

- [Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems](https://mlsanity.com/paper/2602.19253/) (2026)

## Abstract (arXiv)

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.