域适应在生态学与水文学机器学习中提升模型外推能力的潜力
Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability
AI总结：
本文指出生态水文机器学习模型因观测分布不均而外推能力弱，建议采用域适应技术提升模型泛化与外推能力。
摘要：
由于全球生态和水文地面观测分布的不均匀性，机器学习模型在应用于未知地点时适应性有限，这被称为弱外推能力。域适应技术已广泛应用于图像分类等机器学习领域，通过调整训练集与测试集之间域分布的差异或不一致性，可提升模型的泛化能力。然而，在全球尺度上，该方法在生态学和水文学机器学习模型中很少被明确使用，尽管这些模型常因地理外推问题而受到质疑。本文简要描述了当前生态学和水文学机器学习模型在观测分布全球代表性方面的不足，以及由此导致的缺乏外推能力的局限性，并建议未来相关建模工作应考虑使用域适应技术以提高外推能力。
英文摘要：
Due to the heterogeneity of the global distribution of ecological and hydrological ground-truth observations, machine learning models can have limited adaptability when applied to unknown locations, which is referred to as weak extrapolability. Domain adaptation techniques have been widely used in machine learning domains such as image classification, which can improve the model generalization ability by adjusting the difference or inconsistency of the domain distribution between the training and test sets. However, this approach has rarely been used explicitly in machine learning models in ecology and hydrology at the global scale, although these models have often been questioned due to geographic extrapolability issues. This paper briefly describes the shortcomings of current machine learning models of ecology and hydrology in terms of the global representativeness of the distribution of observations and the resulting limitations of the lack of extrapolability and suggests that future related modelling efforts should consider the use of domain adaptation techniques to improve extrapolability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
