  A deeper understanding of the drivers of evapotranspiration and the modelling
of its constituent parts (evaporation and transpiration) could be of
significant importance to the monitoring and management of water resources
globally over the coming decades. In this work, we developed a framework to
identify the best performing machine learning algorithm from a candidate set,
select optimal predictive features as well as ranking features in terms of
their importance to predictive accuracy. Our experiments used 3 separate
feature sets across 4 wetland sites as input into 8 candidate machine learning
algorithms, providing 96 sets of experimental configurations. Given this high
number of parameters, our results show strong evidence that there is no
singularly optimal machine learning algorithm or feature set across all of the
wetland sites studied despite their similarities. A key finding discovered when
examining feature importance is that methane flux, a feature whose relationship
with evapotranspiration is not generally examined, may contribute to further
biophysical process understanding.
