TY - JOUR
TI - Machine learning models to prediction OPIC crude oil production
AU - Abdulrahim Hiyam
AU - Alshibani Safiya Mukhtar
AU - Ibrahim Omer Ibrahim Osman
AU - Elhag Azhari A
JN - Thermal Science
PY - 2022
VL - 26
IS - 101
SP - 437
EP - 443
PT - Article
AB - 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.