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

• URL: http://arxiv.org/abs/2511.13469v1
• Date: Mon, 17 Nov 2025 15:11:03 GMT
• Title: GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction
• Authors: Shiyuan Luo, Chonghao Qiu, Runlong Yu, Yiqun Xie, Xiaowei Jia,
• Abstract summary: Generalizable Representation Enhancement via Auxiliary Transformations (GREAT)<n>We introduce GREAT, a framework that effectively augments available datasets to improve predictions in completely unseen regions.<n>We demonstrate GREAT's effectiveness on stream temperature prediction across six ecologically diverse watersheds in the eastern U.S.
• Score: 27.332543340013814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting [99.4484686548807]
  We propose OmniAir, a semantic topology learning framework tailored for global station-level prediction.<n>Our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks.<n>Experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models.
  arXiv  Detail & Related papers  (2026-01-29T15:58:07Z)
• Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation [60.04281435591454]
  CRDA (Curriculum Reinforcement-Learning Data Augmentation) is a novel framework guiding detectors to progressively master multi-domain forgery features.<n>Central to our approach is integrating reinforcement learning and causal inference.<n>Our method significantly improves detector generalizability, outperforming SOTA methods across multiple cross-domain datasets.
  arXiv  Detail & Related papers  (2025-11-10T12:45:52Z)
• Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments [61.62036321848316]
  Graph neural networks (GNNs) have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs.<n>Existing GNNs exhibit poor ability under distribution shifts, which is inevitable in dynamic scenarios.<n>This paper proposes Evolving Graph Learning framework for evolving graph generalization (Evoal) by environment-aware invariant pattern recognition.
  arXiv  Detail & Related papers  (2025-11-04T08:22:29Z)
• FrogDeepSDM: Improving Frog Counting and Occurrence Prediction Using Multimodal Data and Pseudo-Absence Imputation [0.9537146822132906]
  Species Distribution Modelling (SDM) helps predict species presence across large regions.<n>In this study, we enhance SDM accuracy for frogs (Anura) by applying deep learning and data imputation techniques.<n>Experiments show that data balancing significantly improved model performance, reducing the Mean Absolute Error (MAE) from 189 to 29 in frog counting tasks.
  arXiv  Detail & Related papers  (2025-10-22T07:09:36Z)
• Globalization for Scalable Short-term Load Forecasting [7.654516721062505]
  This paper investigates global load forecasting in the presence of data drifts.<n>We show how globalization, data heterogeneity, and data drift affect each differently.<n>We also examine the role of globalization in peak load forecasting and its potential for hierarchical forecasting.
  arXiv  Detail & Related papers  (2025-07-15T20:58:14Z)
• PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization [12.15086255236961]
  We show that the performance of such augmentation-based methods in the target domains universally fluctuates during training.<n>We propose a novel generalization method, coined.<n>Space Ensemble with Entropy Regularization (PEER), that uses a proxy model to learn the augmented data.
  arXiv  Detail & Related papers  (2025-05-19T06:01:11Z)
• Counterfactual experience augmented off-policy reinforcement learning [9.77739016575541]
  CEA builds efficient inference model and enhances representativeness of learning data.<n>Uses variational autoencoders to model the dynamic patterns of state transitions.<n>Builds a complete counterfactual experience to alleviate the out-of-distribution problem of the learning data.
  arXiv  Detail & Related papers  (2025-03-18T02:32:50Z)
• Trajectory World Models for Heterogeneous Environments [67.27233466954814]
  Heterogeneity in sensors and actuators across environments poses a significant challenge to building large-scale pre-trained world models.<n>We introduce UniTraj, a unified dataset comprising over one million trajectories from 80 environments, designed to scale data while preserving critical diversity.<n>We also propose TrajWorld, a novel architecture capable of handling varying sensor and actuator information and capturing environment dynamics in-context.
  arXiv  Detail & Related papers  (2025-02-03T13:59:08Z)
• SPARTAN: A Sparse Transformer Learning Local Causation [63.29645501232935]
  Causal structures play a central role in world models that flexibly adapt to changes in the environment. We present the SPARse TrANsformer World model (SPARTAN), a Transformer-based world model that learns local causal structures between entities in a scene. By applying sparsity regularisation on the attention pattern between object-factored tokens, SPARTAN identifies sparse local causal models that accurately predict future object states.
  arXiv  Detail & Related papers  (2024-11-11T11:42:48Z)
• Generative adversarial networks for data-scarce spectral applications [0.0]
  We report on an application of GANs in the domain of synthetic spectral data generation. We show that CWGANs can act as a surrogate model with improved performance in the low-data regime.
  arXiv  Detail & Related papers  (2023-07-14T16:27:24Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.