Knowledge-Based Dynamic Systems Modeling: A Case Study on Modeling River
Water Quality
Abstract
Modeling real-world phenomena is a focus of many science and engineering
efforts, from ecological modeling to financial forecasting. Building an
accurate model for complex and dynamic systems improves understanding of
underlying processes and leads to resource efficiency. Knowledge-driven
modeling builds a model based on human expertise, yet is often
suboptimal. At the opposite extreme, data-driven modeling learns a model
directly from data, requiring extensive data and potentially generating
overfitting. We focus on an intermediate approach, model revision, in
which prior knowledge and data are combined to achieve the best of both
worlds. We propose a genetic model revision framework based on
tree-adjoining grammar (TAG) guided genetic programming (GP), using the
TAG formalism and GP operators in an effective mechanism making
data-driven revisions while incorporating prior knowledge. Our framework
is designed to address the high computational cost of evolutionary
modeling of complex systems. Via a case study on the challenging problem
of river water quality modeling, we show that the framework efficiently
learns an interpretable model, with higher modeling accuracy than
existing methods.