THERMAL SCIENCE

International Scientific Journal

Hiyam Abdulrahim

,

Safiya Mukhtar Alshibani

,

Omer Ibrahim Osman Ibrahim

,

Azhari A. Elhag

MACHINE LEARNING MODELS TO PREDICTION OPIC CRUDE OIL PRODUCTION

ABSTRACT
This paper aimed to compare the multi-layer perceptron as an artificial neural network and the decision tree model for predicting OPIC crude oil production. Machine learning is about designing algorithms that automatically extract valuable information from data, and it has seen many success stories. The accuracy of these two models was assessed using symmetric mean absolute percentage errors, mean absolute scaled errors, and mean absolute percentage errors. Achieved were the OPIC crude oil production's maximum projected figures. The OPIC crude oil output was also represented by certain descriptive scales and graphs; A comparison was made between the results and the earlier results acquired by the others after the study of the association between the variables revealed statistical significance.
KEYWORDS
machine learning, Artificial Neural Network, symmetric mean absolute percentage errors, mean absolute percentage error
PAPER SUBMITTED: 2022-10-15
PAPER REVISED: 2022-11-22
PAPER ACCEPTED: 2022-11-28
PUBLISHED ONLINE: 2023-01-21
DOI REFERENCE: https://doi.org/10.2298/TSCI22S1437A
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Special issue 1, PAGES [437 - 443]
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Machine learning models to prediction OPIC crude oil production

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence