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 MSGP-LASSO 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-least-square models. It is explicit and promising for real-life applications.

本文介绍了一种称为MSGP-LASSO的新的多阶段遗传编程（MSGP）技术的开发和验证，该技术已用于土耳其间歇河Sedre河的单变量流量预测。MSGP-LASSO是对经典遗传编程（GP）的实用且成本中立的改进，可以提高建模精度，同时通过将MSGP和多元回归LASSO耦合来降低其复杂性方法。新模型使用平均互信息来确定最佳滞后，并使用均方根技术来最小化预测误差。基于Nash-Sutcliffe效率和经过偏差校正的Akaike信息准则，MSGP-LASSO优于GP，多基因GP，MSGP和混合MSGP最小二乘模型。对于现实生活中的应用而言，它是明确且有希望的。