An Intelligent Modeling of Oil Consumption

• Main Authors: Chiroma, Haruna, Abdulkareem, Sameem, Muaz, Sanah Abdullahi, Abubakar, Adamu I., Sutoyo, Edi, Mungad , Mungad, Saadi, Younes, Sari, Eka Novita, Tutut, Herawan
• Format: Book Section
• Published: Springer International Publishing 2015
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/8251/; http://dx.doi.org/10.1007/978-3-319-11218-3_50

In this study, we select Middle East countries involving Jordan, Lebanon, Oman, and Saudi Arabia for modeling oil consumption based on computational intelligence methods. The limitations associated with Levenberg-Marquardt (LM) Neural Network (NN) motivated this research to optimize the parameters of NN through Artificial Bee Colony Algorithm (ABC-LM) to build a model for the prediction of oil consumption. The proposed model was competent to predict oil consumption with improved accuracy and convergence speed. The ABC-LM performs better than the standard LMNN, Genetically optimized NN, and Back-propagation NN. The proposed model may guide policy makers in the formulation of domestic and international policies related to oil consumption and economic development. The approach presented in the study can easily be implemented into a software for use by the government of Jordan, Lebanon, Oman, and Saudi Arabia.