Induction Of Governing Differential Equations From Hydrologic Time Series Data Using Genetic Programming

Induction of Governing Differential Equations from Hydrologic Time Series Data using Genetic Programming Jayashree Chadalawada and Vladan Babovic This contribution describes an evolutionary method for identifying causal model from the observed time-series data. In the present case, we use a system of ordinary differential equations (ODEs) as the causal model. Usefulness of the approach is demonstrated on real-world time series of hydrologic processes and the unknown function of governing factors are determined. To explore the evolutionary search space more effectively, the right hand sides of ODEs are inferred by genetic programming (GP). The importance of different fitness criteria, as well as introduction of background knowledge about underlying processes are also being discussed and assessed. The method is applied on several cases and empirically demonstrated how successfully GP infers the systems of ODEs