Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System

2026-01-26

AI Summary

This study addresses the critical gap in interpretable uncertainty quantification for energy consumption prediction in wastewater treatment plants, which often hinders risk-sensitive decision-making. The authors propose an Interval Type-2 Adaptive Neuro-Fuzzy Inference System (IT2-ANFIS), introducing—for the first time in this domain—an interpretable three-layer uncertainty decomposition mechanism that explicitly links prediction confidence to operational conditions and input variables. By integrating interval type-2 fuzzy logic with the ANFIS architecture, the method enables coordinated uncertainty analysis at both the feature and rule levels. Experimental results on a dataset from Melbourne Water’s Eastern Treatment Plant demonstrate that IT2-ANFIS achieves prediction accuracy comparable to its type-1 counterpart while significantly reducing training variance and delivering highly interpretable prediction intervals.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

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.

Problem

• Explainable Uncertainty Quantification
• Wastewater Treatment
• Energy Prediction
• Risk-aware Decision-making
• Safety-critical Infrastructure

Innovation

• Explainable Uncertainty Quantification
• Interval Type-2 Neuro-Fuzzy System
• IT2-ANFIS
• Prediction Intervals
• Wastewater Treatment Energy Prediction