MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction

This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGPLASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity by coupling the MSGP and multiple regression LASSO methods. The new model uses average mutual information to identify the optimum lags, and root mean-square technique to minimize forecasting error. Based on Nash-Sutcliffe efficiency and bias-corrected Akaike information criterion, MSGP-LASSO is superior to GP, multigene GP, MSGP, and hybrid MSGP-leastsquare models. It is explicit and promising for real-life applications. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces a new multi-stage genetic programming (MSGP) technique called MSGP-LASSO, which has been successfully applied for univariate streamflow forecasting in the Sedre River in Turkey. MSGP-LASSO has been verified to be more reliable and effective in flood forecasting compared to traditional genetic programming techniques.