67,029 research outputs found
Cohort aggregation modelling for complex forest stands: Spruce-aspen mixtures in British Columbia
Mixed-species growth models are needed as a synthesis of ecological knowledge
and for guiding forest management. Individual-tree models have been commonly
used, but the difficulties of reliably scaling from the individual to the stand
level are often underestimated. Emergent properties and statistical issues
limit their effectiveness. A more holistic modelling of aggregates at the whole
stand level is a potentially attractive alternative. This work explores
methodology for developing biologically consistent dynamic mixture models where
the state is described by aggregate stand-level variables for species or
age/size cohorts. The methods are demonstrated and tested with a two-cohort
model for spruce-aspen mixtures named SAM. The models combine single-species
submodels and submodels for resource partitioning among the cohorts. The
partitioning allows for differences in competitive strength among species and
size classes, and for complementarity effects. Height growth reduction in
suppressed cohorts is also modelled. SAM fits well the available data, and
exhibits behaviors consistent with current ecological knowledge. The general
framework can be applied to any number of cohorts, and should be useful as a
basis for modelling other mixed-species or uneven-aged stands.Comment: Accepted manuscript, to appear in Ecological Modellin
Rule-based Machine Learning Methods for Functional Prediction
We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction of compact, easily interpretable solutions. This rule-based
decision model can be extended to search efficiently for similar cases prior to
approximating function values. Experimental results on real-world data
demonstrate that the new techniques are competitive with existing machine
learning and statistical methods and can sometimes yield superior regression
performance.Comment: See http://www.jair.org/ for any accompanying file
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