Integrating knowledge-driven and data-driven approaches to modeling

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 for combining 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.