Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting

This paper focuses on improving rainfall-runoff forecasts by a combination of genetic programming (GP) and basic hydrological modelling concepts. GP is a general optimisation technique for making an automated search of a computer program that solves some particular problem. The SORD! program was developed for the purposes of this study (in the R programming language). It is an implementation of canonical GP. Special functions are used for a combined approach of hydrological concepts and GP. The special functions are a reservoir model, a simple moving average model, and a cumulative sum and delay operator. The efficiency of the approach presented here is tested on runoff predictions for five catchments of various sizes. The input data consists of daily rainfall and runoff series. The forecast step is one day. The performance of the proposed approach is compared with the results of the artificial neural network model (ANN) and with the GP model without special functions. GP combined with these concepts provides satisfactory performance, and the simulations seem to be more accurate than the results of ANN and GP without these functions. An additional advantage of the proposed approach is that it is not necessary to determine the input lag, and there is better convergence. The SORD! program provides an easy-to-use alternative for data-oriented modelling combined with simple concepts used in hydrological modelling.