Genetic Programming in Hydrology

Abstract

This report introduces the use of Genetic Programming (GP) into hydrology by describing the results of GP using conceptual hydrological models as physical representation. First the possibilities of GP are tested on synthetic data, which results in a shortlist of good working objective functions and insight in the most important GP settings. The test on real data in the Belgium Ardennes showed that GP using the objective functions KG10, MM and Shafii performed better. Nevertheless all three models performed not well on simulating the low flows and high peaks. Furthermore GP using KG10 and MM both results in simple serial models which perform well overall, but bad on quick response runoff. Shafii resulted in parallel models which show quick response flow, however GP it is not able to capture the fast responses correctly (yet). GP has the potential to improve the understanding in the behaviour of catchments, however it still needs the human mind to observe, compare and analyse the modelling results. The main consideration with GP is to look for a balance between: model search space, objective function, randomness and (computational) time. The challenge is how to lead GP in an efficient way without removing the possibility of finding unknown patterns.