Uncertainty-aware maintenance scheduling in water distribution networks via ensemble neural forecasting and explainable confidence indexing
Maintenance scheduling in water distribution networks (WDNs) needs accurate demand forecasts. However, climate variability makes point predictions insufficient. Existing Digital Twin (DT) frameworks use deterministic forecasts, leading to over-scheduling and Service Level Agreement (SLA) violations during volatile weather. Bayesian uncertainty methods are rigorous but require 340 ms per inference, making them too slow for real-time scheduling on standard utility hardware. We propose CAUCCES, coupling an adaptive ensemble (LSTM, Prophet, LightGBM, XGBoost) with a novel Explainable Confidence Index (ECI). ECI is a closed-form uncertainty measure based on ensemble entropy and variance. It directly connects the forecasting module to a constraint-based scheduler. When confidence drops, non-critical tasks are deferred, turning forecast uncertainty into actionable decisions. Validated across 12 Spanish municipalities over 18 months, ECI-driven scheduling reduces SLA violations from 9.3% to 1.5% with only 3.1% extra operational cost. The ensemble achieves 14.12% MAPE, outperforming DeepAR (16.50%) and Temporal Fusion Transformer (16.92%) Furthermore, it achieves a 28\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} speedup (12 ms vs. 340 ms) compared to a Bayesian LSTM baseline. Finally, multi-regional testing shows 12–26% performance degradation across different climatic zones, showing that local recalibration is necessary. These results suggest that entropy-based uncertainty quantification can serve as a practical bridge between forecasting and scheduling for resource-constrained utilities, although broader validation across climates and operating conditions is still needed.