MACHINE LEARNING MODELS TO PREDICTION OPIC CRUDE OIL PRODUCTION

被引：0

作者：

Abdulrahim, Hiyam ^{[1
]}

Alshibani, Safiya Mukhtar ^{[2
]}

Ibrahim, Omer Ibrahim Osman ^{[3
]}

Elhag, Azhari A. ^{[4
]}

机构：

[1] Princess Nourah Bint Abdulrahman Univ, Coll Business & Adm, Dept Econ, Riyadh, Saudi Arabia

[2] Princess Nourah Bint Abdulrahman Univ, Coll Business & Adm, Dept Business Adm, Riyadh, Saudi Arabia

[3] Rania Taif Univ, Univ Coll, Dept Sci & Technol, Math Program, Taif, Saudi Arabia

[4] Taif Univ, Coll Sci, Dept Math, Taif, Saudi Arabia

来源：

THERMAL SCIENCE | 2022年 / 26卷

关键词：

machine learning; artificial neural network; symmetric mean absolute percentage errors; mean absolute percentage error;

D O I：

暂无

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

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 valu-able information from data, and it has seen many success stories. The accuracy of these two models was assessed using symmetric mean absolute percentage er-rors, 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 ac-quired by the others after the study of the association between the variables re-vealed statistical significance.

引用

页码：S437 / S443

页数：7

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