用可解释的模糊系统预测污水厂能耗不确定性，助力安全决策。
Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System
- 基于区间2型模糊神经网络生成可解释预测区间
- 在墨尔本污水处理厂数据上误差与传统模型相当但结果更稳定
- 分三层解析不确定性，关联具体运行条件和变量
污水处理厂消耗全球1%-3%的电力，精准能源预测对运营优化和可持续性至关重要。尽管机器学习能提供点预测，却缺乏可解释的不确定性量化，难以支持安全关键基础设施的风险决策。本文提出一种区间2型自适应神经模糊推理系统（IT2-ANFIS），通过模糊规则结构生成可解释的预测区间。不同于黑箱概率方法，该框架将不确定性分解为三个层面：特征级识别引入模糊性的变量，规则级分析局部模型置信度，实例级区间量化整体预测不确定性。在墨尔本水务东区处理厂数据集上验证，IT2-ANFIS在预测性能上与一阶ANFIS相当，训练结果方差显著降低，同时提供可追溯至运行条件和输入变量的可解释不确定性估计。
原文摘要 · Abstract (English)
Wastewater treatment plants consume 1-3% of global electricity, making accurate energy forecasting critical for operational optimization and sustainability. While machine learning models provide point predictions, they lack explainable uncertainty quantification essential for risk-aware decision-making in safety-critical infrastructure. This study develops an Interval Type-2 Adaptive Neuro-Fuzzy Inference System (IT2-ANFIS) that generates interpretable prediction intervals through fuzzy rule structures. Unlike black-box probabilistic methods, the proposed framework decomposes uncertainty across three levels: feature-level, footprint of uncertainty identify which variables introduce ambiguity, rule-level analysis reveals confidence in local models, and instance-level intervals quantify overall prediction uncertainty. Validated on Melbourne Water's Eastern Treatment Plant dataset, IT2-ANFIS achieves comparable predictive performance to first order ANFIS with substantially reduced variance across training runs, while providing explainable uncertainty estimates that link prediction confidence directly to operational conditions and input variables.
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