Performance evaluation of Gene Expression Programming for hydraulic data mining
Predication is one of the fundamental tasks of data mining. In recent years, Artificial Intelligence techniques are widely being used in data mining applications where conventional statistical methods were used such as Regression and classification. The aim of this work is to show the applicability of Gene Expression Programming (GEP), a recently developed AI technique, for hydraulic data prediction and to evaluate its performance by comparing it with Multiple Linear Regression (MLR). Both GEP and MLR were used to model the hydraulic jump over a roughened bed using very large series of experimental data that contain all the important flow and roughness parameters such as the initial Froude number, the height of roughness ratio, the length of roughness ratio, the initial length ratio (from the gate) and the roughness density. The results show that GEP is a promising AI approach for hydraulic data prediction.
gene expression programming data mining gep multiple linear regression mlr hydraulic jump