HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction

2025-12-02

AI Summary

To address domain shift in cross-reservoir inflow forecasting, this paper proposes a scalable domain generalization (DG) framework tailored for hydrological systems. Unlike conventional DG approaches relying solely on domain-invariant representations, our method introduces reservoir spatial metadata to construct pseudo-domain labels and designs a lightweight conditional modulation layer for location-adaptive temporal feature modeling. Furthermore, adversarial learning is integrated to extract domain-invariant time-series features, enabling dynamic inference-time adaptation to unseen target reservoirs. Evaluated on 30 real-world reservoirs in the Upper Colorado River Basin, the proposed framework significantly outperforms existing DG baselines in both accuracy and efficiency—achieving state-of-the-art performance with low computational overhead. This makes it particularly suitable for large-scale operational hydrological forecasting.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differences, referred to as the domain shift problem. Domain generalization (DG) solutions aim to address this issue by extracting domain-invariant representations that mitigate errors in unseen domains. However, in hydrological settings, each reservoir exhibits unique inflow patterns, while some metadata beyond observations like spatial information exerts indirect but significant influence. This mismatch limits the applicability of conventional DG techniques to many-domain hydrological systems. To overcome these challenges, we propose HydroDCM, a scalable DG framework for cross-reservoir inflow forecasting. Spatial metadata of reservoirs is used to construct pseudo-domain labels that guide adversarial learning of invariant temporal features. During inference, HydroDCM adapts these features through light-weight conditioning layers informed by the target reservoir's metadata, reconciling DG's invariance with location-specific adaptation. Experiment results on 30 real-world reservoirs in the Upper Colorado River Basin demonstrate that our method substantially outperforms state-of-the-art DG baselines under many-domain conditions and remains computationally efficient.

Problem

• Addresses domain shift in reservoir inflow prediction across different reservoirs
• Uses spatial metadata to guide invariant feature learning for generalization
• Adapts features for target reservoirs via lightweight conditioning layers

Innovation

• Adversarial learning with pseudo-domain labels
• Light-weight conditioning layers for adaptation
• Metadata-guided invariant feature extraction