GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction

2025-11-17

AI Summary

Environmental modeling faces zero-shot cross-regional prediction challenges due to sparse observations and strong spatial heterogeneity, while existing domain generalization methods struggle to preserve physical consistency and temporal continuity. This paper proposes a generalizable representation enhancement framework for zero-shot environmental forecasting: it designs hierarchical transformation functions to disentangle static environmental features from dynamic temporal effects, implementing a multi-layer neural network augmented with auxiliary transformations; and introduces a bilevel optimization scheme that learns invariant representations on enhanced data while enforcing constraints to recover the original dominant physical patterns—ensuring both physical interpretability and temporal coherence. Experiments across six ecologically distinct watersheds in the eastern United States demonstrate that our method significantly outperforms state-of-the-art models, achieving, for the first time under zero-shot settings, high-accuracy, physically consistent cross-regional predictions.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This challenge is compounded by spatial heterogeneity, causing models to learn spurious patterns that fit only local data. Unlike conventional domain generalization, environmental modeling must preserve invariant physical relationships and temporal coherence during augmentation. In this paper, we introduce Generalizable Representation Enhancement via Auxiliary Transformations (GREAT), a framework that effectively augments available datasets to improve predictions in completely unseen regions. GREAT guides the augmentation process to ensure that the original governing processes can be recovered from the augmented data, and the inclusion of the augmented data leads to improved model generalization. Specifically, GREAT learns transformation functions at multiple layers of neural networks to augment both raw environmental features and temporal influence. They are refined through a novel bi-level training process that constrains augmented data to preserve key patterns of the original source data. We demonstrate GREAT's effectiveness on stream temperature prediction across six ecologically diverse watersheds in the eastern U.S., each containing multiple stream segments. Experimental results show that GREAT significantly outperforms existing methods in zero-shot scenarios. This work provides a practical solution for environmental applications where comprehensive monitoring is infeasible.

Problem

• Predicting ecosystem dynamics in unmonitored regions with limited data
• Addressing spatial heterogeneity that causes spurious local patterns
• Preserving invariant physical relationships during data augmentation

Innovation

• Learns transformation functions at multiple network layers
• Refines transformations via novel bi-level training process
• Augments raw environmental features and temporal influence