River flow forecasting using gene expression programming models

Abstract

River flow forecasting models provide an essential tool to manage water resources, address problems associated with both excesses and deficits, and to find suitable solutions. With changing climate and environmental conditions, real-time methods that rely on data-driven methods of river flow forecasting has become appropriate enabling the use of real data from the recent past rather than relying on models based on the underlying hydrology of the catchment(s). This paper investigates the application of the novel datadriven technique of Gene Expression Programming (GEP) to develop one-day-ahead flow forecasting models for catchments with widely differing characteristics. The method differs from other more hitherto popular data-driven techniques that produce “Black-Box” models in that it generates a transparent model with a mathematical expression for the mapping from input parameters such as antecedent rainfall/runoff to the forecast flow. Four GEP models using GenXproTools® software developed for four catchments show that accurate forecasts fit for purpose can be made from these transparent models.