A Genetic Programming Approach to System Identification of Rainfall-Runoff Models

Advancements in data acquisition, storage and retrieval are progressing at an extraordinary rate, whereas the same in the field of knowledge extraction from data is yet to be accomplished. The challenges associated with hydrological datasets, including complexity, non-linearity and multicollinearity, motivate the use of machine learning to build hydrological models. Increasing global climate change and urbanization call for better understanding of altered rainfall-runoff processes. There is a requirement that models are intelligible estimates of underlying physics, coupling explanatory and predictive components, maintaining parsimony and accuracy. Genetic Programming, an evolutionary computation technique has been used for short-term prediction and forecast in the field of hydrology. Advancing data science in hydrology can be achieved by tapping the full potential of GP in defining an evolutionary flexible modelling framework that balances prior information, simulation accuracy and strategy for future uncertainty. As a preliminary step, GP is used in conjunction with a conceptual rainfall-runoff model to solve model configuration problem. Two datasets belonging to a tropical catchment of Singapore and a temperate catchment of South Island, New Zealand with contrasting characteristics are analyzed in this study. The results indicate that proposed approach successfully combines the merits of evolutionary algorithm and conceptual knowledge in the generation of optimal model structure and associated parameters to capture runoff dynamics of catchments.