综述大模型在环境科学中的应用，助力生态预测与可持续发展
A Survey of Foundation Models for Environmental Science
- 系统梳理大模型在环境领域的应用方法与技术流程
- 涵盖预测、数据生成、降尺度等多任务场景的进展
- 适合跨学科研究者了解机器学习在环境中的前沿实践
环境生态系统建模对于资源管理、可持续发展及理解复杂生态过程至关重要。然而，传统方法常受限于系统固有的复杂性、相互关联性以及数据稀缺问题。基础模型凭借大规模预训练和通用表征能力，为整合多源数据、捕捉时空依赖关系并适应多样化任务提供了变革性机遇。本综述全面概述了基础模型在环境科学中的应用，重点涵盖正向预测、数据生成、数据同化、降尺度、模型集成及决策支持等多个领域。同时，详述了模型开发全过程，包括数据收集、架构设计、训练、调优与评估。通过展示这些新兴方法，旨在促进跨学科协作，推动前沿机器学习技术在环境科学中的融合应用，实现可持续解决方案。
原文摘要 · Abstract (English)
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity, interconnectedness, and limited data of such systems. Foundation models, with their large-scale pre-training and universal representations, offer transformative opportunities by integrating diverse data sources, capturing spatiotemporal dependencies, and adapting to a broad range of tasks. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in forward prediction, data generation, data assimilation, downscaling, model ensembling, and decision-making across domains. We also detail the development process of these models, covering data collection, architecture design, training, tuning, and evaluation. By showcasing these emerging methods, we aim to foster interdisciplinary collaboration and advance the integration of cutting-edge machine learning for sustainable solutions in environmental science.
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