提升反渗透海水淡化预测模型的可解释性，让专家能理解决策依据。
Interpretable Fuzzy Systems For Forward Osmosis Desalination
- 专家参与划分模糊集，增强规则语义可读性。
- 在保持预测性能的同时，满足结构复杂度约束。
- 适合关注模型可解释性的水处理领域研究者。
在水处理领域，模糊规则系统（FRBS）的可解释性至关重要，因决策直接关系公共健康。尽管结构可解释性可通过多目标算法解决，但语义可解释性常因模糊集区分度低而受损。本文提出一种人机协同方法，用于构建可解释的FRBS以预测反渗透海水淡化产水量。该方法结合专家驱动的网格划分以生成可区分的隶属函数、基于领域知识的特征工程以减少冗余，并根据触发强度进行规则剪枝。实验表明，该方法在保持与聚类法相当预测性能的同时，确保了语义可解释性并满足结构复杂度限制，为水处理应用提供了可解释的解决方案。
原文摘要 · Abstract (English)
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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