  As the costs of sensors and associated IT infrastructure decreases - as
exemplified by the Internet of Things - increasing volumes of observational
data are becoming available for use by environmental scientists. However, as
the number of available observation sites increases, so too does the
opportunity for data quality issues to emerge, particularly given that many of
these sensors do not have the benefit of official maintenance teams. To realise
the value of crowd sourced 'Internet of Things' type observations for
environmental modelling, we require approaches that can automate the detection
of outliers during the data modelling process so that they do not contaminate
the true distribution of the phenomena of interest. To this end, here we
present a Bayesian deep learning approach for spatio-temporal modelling of
environmental variables with automatic outlier detection. Our approach
implements a Gaussian-uniform mixture density network whose dual purposes -
modelling the phenomenon of interest, and learning to classify and ignore
outliers - are achieved simultaneously, each by specifically designed branches
of our neural network. For our example application, we use the Met Office's
Weather Observation Website data, an archive of observations from around 1900
privately run and unofficial weather stations across the British Isles. Using
data on surface air temperature, we demonstrate how our deep mixture model
approach enables the modelling of a highly skilled spatio-temporal temperature
distribution without contamination from spurious observations. We hope that
adoption of our approach will help unlock the potential of incorporating a
wider range of observation sources, including from crowd sourcing, into future
environmental models.
