Interpretable Fuzzy Systems For Forward Osmosis Desalination

TL;DR

This paper introduces a human-in-the-loop method for creating interpretable fuzzy rule-based systems to predict forward osmosis desalination productivity, balancing accuracy with semantic clarity for water treatment.

Contribution

It presents a novel approach combining expert-driven partitioning, domain-guided feature engineering, and rule pruning to enhance interpretability without sacrificing predictive performance.

Findings

Achieved comparable accuracy to cluster-based FRBS.

Maintained semantic interpretability and structural simplicity.

Provided an explainable water treatment prediction model.

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.