Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model

Abstract

A precise forecast of streamflow in intermittent rivers is of major difficulties and challenges in watershed management, particularly in arid and semiarid regions. The present research study introduces an ensemble gene expression programming (EGEP) modeling approach to 1- and 2-day ahead streamflow forecasts that meet both accuracy and simplicity criteria of an applied model. Three main components of the proposed EGEP approach which are capable of producing a parsimonious model include (i) creating a population of suitable solutions using classic genetic programming (GP) instead of a single solution, (ii) combining the solutions throughout gene expression programming, and (iii) parsimony selection based upon trade-off analysis between the complexity and accuracy of the best-evolved solutions at the holdout validation set. The EGEP model was trained and verified using the streamflow measurements from the Shahrchay River lying northwest of Iran. Several statistical indicators were computed for verification of the ensemble models’ accuracy with that of classic GP and artificial neural network models developed as the benchmarks. Our results revealed that the EGEP outperforms the benchmarks. It is an explicit, simple, and precise approach and, therefore, worthy to be used in practice.