Potential of Domain Adaptation in Machine Learning in Ecology and
  Hydrology to Improve Model Extrapolability
Haiyang Shi
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.