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

TL;DR

GREAT is a novel framework that enhances environmental data representations through auxiliary transformations, improving zero-shot ecosystem predictions while preserving physical and temporal data integrity.

Contribution

The paper introduces GREAT, a method that learns layered transformations for data augmentation, ensuring invariant physical relationships and temporal coherence in environmental modeling.

Findings

GREAT significantly outperforms existing methods in zero-shot stream temperature prediction.

The framework effectively preserves key physical and temporal patterns during augmentation.

Experimental results across six watersheds demonstrate improved generalization in diverse ecological settings.

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.