Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929281145241600 |
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| author | Shi, Haiyang |
| author_facet | Shi, Haiyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability Shi, Haiyang Geophysics Machine Learning Data Analysis, Statistics and Probability 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. |
| title | Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability |
| topic | Geophysics Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2403.11331 |