  With the underlying aim of increasing efficiency of computational modelling
pertinent for managing & protecting the Great Barrier Reef, we perform a
preliminary investigation on the use of deep neural networks for opportunistic
model emulation of APSIM models by repurposing an existing large dataset
containing outputs of APSIM model runs. The dataset has not been specifically
tailored for the model emulation task. We employ two neural network
architectures for the emulation task: densely connected feed-forward neural
network (FFNN), and gated recurrent unit feeding into FFNN (GRU-FFNN), a type
of a recurrent neural network. Various configurations of the architectures are
trialled. A minimum correlation statistic is used to identify clusters of APSIM
scenarios that can be aggregated to form training sets for model emulation. We
focus on emulating 4 important outputs of the APSIM model: runoff, soil_loss,
DINrunoff, Nleached. The GRU-FFNN architecture with three hidden layers and 128
units per layer provides good emulation of runoff and DINrunoff. However,
soil_loss and Nleached were emulated relatively poorly under a wide range of
the considered architectures; the emulators failed to capture variability at
higher values of these two outputs. While the opportunistic data available from
past modelling activities provides a large and useful dataset for exploring
APSIM emulation, it may not be sufficiently rich enough for successful deep
learning of more complex model dynamics. Design of Computer Experiments may be
required to generate more informative data to emulate all output variables of
interest. We also suggest the use of synthetic meteorology settings to allow
the model to be fed a wide range of inputs. These need not all be
representative of normal conditions, but can provide a denser, more informative
dataset from which complex relationships between input and outputs can be
learned.
