Inducing Process-Based Models of Dynamic Systems from Multiple Data Sets
نویسندگان
چکیده
In this paper, we explore different modeling scenarios for inducing process-based models from multiple data sets. Namely, when modeling ecosystems, environmentalists expect a single model structure to explain system behavior among different yearly seasons, while the values of the constant model parameters may vary from season to season. We confront this modeling scenario with several others corresponding to multiple (one per season) structure or alternative single-structure models. The empirical evaluation and comparison of these scenarios on two tasks of modeling aquatic ecosystems confirm the expectations: single model structure can explain long-term system behavior, but the values of the model parameters for different seasons are significantly different.
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