Journal
ECOLOGICAL MODELLING
Volume 194, Issue 1-3, Pages 3-13Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ecolmodel.2005.10.001
Keywords
computational scientific discovery; machine learning; dynamic systems; aquatic ecosystems; hydrodynamics
Categories
Ask authors/readers for more resources
In this paper, we present a framework for modeling dynamic systems that integrates the knowledge-based theoretical approach to modeling with the data-driven empirical modeling. The framework allows for integration of modeling knowledge specific to the domain of interest in the process of model induction from measured data. The knowledge is organized around the central notion of basic processes in the domain and it includes models thereof as well as guidelines forcombining models of individual processes into a model of the entire observed system. The presented framework is applied to three tasks of modeling dynamic environmental systems from noisy measurement data in the domains of population and hydro dynamics. in all applications, the models induced with the framework can be used both to accurately predict and explain the behavior of the observed dynamic systems. (c) 2005 Elsevier B.V All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available