Performance evaluation of gene expression programming for hydraulic data mining
Joint Authors
al-Drandali, Khalid Ali
Abd al-Azim, Najm
Source
The International Arab Journal of Information Technology
Issue
Vol. 5, Issue 2 (30 Apr. 2008), pp.126-131, 6 p.
Publisher
Publication Date
2008-04-30
Country of Publication
Jordan
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
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.
American Psychological Association (APA)
al-Drandali, Khalid Ali& Abd al-Azim, Najm. 2008. Performance evaluation of gene expression programming for hydraulic data mining. The International Arab Journal of Information Technology،Vol. 5, no. 2, pp.126-131.
https://search.emarefa.net/detail/BIM-10593
Modern Language Association (MLA)
al-Drandali, Khalid Ali& Abd al-Azim, Najm. Performance evaluation of gene expression programming for hydraulic data mining. The International Arab Journal of Information Technology Vol. 5, no. 2 (Apr. 2008), pp.126-131.
https://search.emarefa.net/detail/BIM-10593
American Medical Association (AMA)
al-Drandali, Khalid Ali& Abd al-Azim, Najm. Performance evaluation of gene expression programming for hydraulic data mining. The International Arab Journal of Information Technology. 2008. Vol. 5, no. 2, pp.126-131.
https://search.emarefa.net/detail/BIM-10593
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 131
Record ID
BIM-10593