Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction

2025-11-10

AI Summary

Conventional reservoir inflow forecasting typically employs single-reservoir modeling, neglecting dynamic spatial dependencies among interconnected reservoirs. Method: We propose AdaTrip, a novel framework integrating adaptive dynamic graph learning with a spatiotemporal Transformer to construct a time-varying directed hydrological dependency graph. AdaTrip enables cross-reservoir parameter sharing and automatically identifies critical spatiotemporal dependencies by tightly coupling graph structure learning with attention mechanisms. It further supports decision-oriented interpretability through attention-based visualizations (e.g., attention maps). Results: Evaluated on 30 reservoirs in the Upper Colorado River Basin, AdaTrip significantly outperforms state-of-the-art baselines—especially under data-scarce conditions—demonstrating superior generalizability and interpretability. The learned dynamic graphs reveal physically meaningful hydrological relationships, validating both the model’s efficacy and its capacity for domain-informed inference.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interconnected reservoirs. We introduce AdaTrip as an adaptive, time-varying graph learning framework for multi-reservoir inflow forecasting. AdaTrip constructs dynamic graphs where reservoirs are nodes with directed edges reflecting hydrological connections, employing attention mechanisms to automatically identify crucial spatial and temporal dependencies. Evaluation on thirty reservoirs in the Upper Colorado River Basin demonstrates superiority over existing baselines, with improved performance for reservoirs with limited records through parameter sharing. Additionally, AdaTrip provides interpretable attention maps at edge and time-step levels, offering insights into hydrological controls to support operational decision-making. Our code is available at https://github.com/humphreyhuu/AdaTrip.

Problem

• Predicts multi-reservoir inflow while capturing spatial dependencies
• Learns dynamic hydrological connections using attention mechanisms
• Improves forecasting for reservoirs with limited historical records

Innovation

• Adaptive graph learning with transformer for inflow prediction
• Dynamic graphs with attention mechanisms identify dependencies
• Parameter sharing improves performance for limited data reservoirs