Name
Title | ||
|---|---|---|
MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2021.02.011 | Information Sciences |
Keywords | DocType | Volume |
Genetic programming,LASSO,Multiple regression,Time series modeling,Streamflow,Sedre River | Journal | 561 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Danandeh Mehr | 1 | 5 | 1.50 |
| Amir Hossein Gandomi | 2 | 1836 | 110.25 |