• Jun 14, 2017 News!Vol.8, No.5 has been indexed by EI (Inspec).   [Click]
  • Nov 09, 2017 News!Vol.9, No.5 has been published with online version. 16 peer reviewed articles from 11 specific areas are published in this issue.   [Click]
  • Jul 19, 2017 News!Vol.9, No.4 has been published with online version. 16 peer reviewed articles from 16 specific areas are published in this issue.   [Click]
General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2012 Vol.4(3): 446-447 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2012.V4.504

Prediction of Oil Production with: Data Mining, Neuro-Fuzzy and Linear Regression

Zahra Mahdavi , Maryam Khademi

Abstract—According to importance and usage of researches of petroleum production, our major goal in this paperis to forecast the oil production by using Data Mining Technique for improving estimation of oil consumption base of History of data. The implement of auto regression, Data cleaning and concept of pre-processing to viewpoint of Time series analysis are traditional concept in intelligent format. We use data cleaning for integration data and auto regression to determine input of model and for pre-processing for upgrade operation of ANFIS. ANFIS algorithm is developed by different data pre-processing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. However, it is concluded that ANFIS provides better results than AR in Canada. This is unlike previous expectations that ANFIS always provides better estimation than conventional approaches.

Index Terms—Time series, neuro-fuzzy system, ANFIS, datamining, oil production forecasting.

[PDF]

Cite: Zahra Mahdavi , Maryam Khademi, "Prediction of Oil Production with: Data Mining, Neuro-Fuzzy and Linear Regression," International Journal of Computer Theory and Engineering vol. 4, no. 3, pp.  446-447, 2012.

Copyright © 2008-2015. International Journal of Computer Theory and Engineering. All rights reserved.
E-mail: ijcte@vip.163.com