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.
MohammadHossein Homaei, Óscar Mogollón-Gutiérrez, Mostafa M. Rezaee et al.· Neural computing & applicati...· 0 citations
Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain by jointly optimizing source classification and domain-alignment objectives. A key practical challenge is selecting the trade-off coefficient λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}, which controls the strength of domain alignment. Fixed values and manually designed schedules are commonly used, but the appropriate balance can vary across datasets, transfer directions, and training stages. This paper proposes Adaptive Gradient-Norm Weighting (AWD), a lightweight scalarization mechanism that dynamically adjusts λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document} using the ratio between the gradient norms of the classification and alignment losses. AWD introduces no additional learnable parameters and can be applied to UDA objectives of the form LCE+λLAlign\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {L}_{\textrm{CE}}+\lambda \mathcal {L}_{\textrm{Align}}$$\end{document}. We evaluate AWD within Deep Adaptation Network (DAN) and Domain-Adversarial Neural Network (DANN) on Office-31, Office-Home, DomainNet, and digit adaptation benchmarks using five independent seeds. Across the evaluated benchmarks, AWD consistently improves average performance over fixed and scheduled baselines, with the strongest gains on more difficult transfer settings. On Office-Home with DANN, AWD improves average accuracy over fixed λ=1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda {=}1$$\end{document} by +4.4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+4.4$$\end{document} percentage points, while avoiding manual per-task grid search. The method incurs only modest computational overhead. Overall, the results show that gradient-aware adaptive weighting is a simple, practical, and interpretable mechanism for balancing classification and alignment losses in UDA.
Iman Khazrak, Mohammadhossein Homaei, Mostafa M. Rezaee et al.· International Journal of Dat...· 0 citations
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