HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

e a subset of the set of good fit models selected with two of the three time-series
previously mentioned. This puts an important emphasis on which measurements
and observations added to the search. The decision of adding an extra time-series to
the software should be highly influenced by which data has already been collected
and used with HIPM. That being said this could be an area where HIPM could be
enhanced and a direction for further research. Indeed, it would be interesting to look
at the output of HIPM if the reMSE was calculated by taking the maximum square
error of all the variables being fitted which would then make our assumption, that
more time-series data equates fewer models, valid.
The parameter selection process
is a very important step of HIPM model selection; this statement was reinforced
by the mathematical analysis that made evident the sensitivity of the system to parameter
values. Differences in parameter values could mean the difference between a
population going to extinction or not. In the case of nitrate and iron I observed that
specific parameters acted as bounds for these variables, which is useful information.
Indeed, when looking at a model through mathematical analysis one can determine
if a parameter will have a significant effect on the overall dynamics of the system.
Coupling this information with experts knowledge it would then be possible to redefine
the ranges set within HIPM for the parameter selection, which would in turn
refine the search process. Scientists could then run the software once again, take
the result and see if parameters ranges could be further refined. This can almost be
seen as a cycle, deriving information on the parameters from the analytical analysis
which in turn help better the constraints given to HIPM, then repeating the process
to see the improvement made to the type of model being selected. This in itself can
be seen as a procedure that transcend just the phytoplankton dynamic in the Ross
Sea ecosystem and can be generalized to other systems.
Automated modeling is a successful method, the LAGRAMGE framework has
been successfully applied in a real-world domain and selected models that performed
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