Leveraging Support Vector Machine for Predictive Analysis of Earned Value Performance Indicators in Iraq's Oil Projects
Bibliographic record
Abstract
Although earned value management (EVM) offers considerable advantages for schedule and cost control within oil projects, its implementation as a project control technique remains limited in Iraq.This study primarily aims to establish predictive models, utilizing support vector machine (SVM), to estimate earned value performance indicators namely schedule performance index (SPI), cost performance index (CPI), and to-complete cost performance index (TCPI) within the context of Iraqi oil projects.The dataset, encompassing 83 monthly reports spanning from 26th June 2015 to 25th August 2022, was sourced from the Karbala Refinery Project.This project, managed by the oil projects company (SCOP) under the Iraqi Ministry of Oil, represents one of the largest and most contemporary initiatives within the region.The results revealed significant findings, including an average accuracy (AA%) for CPI, SPI, and TCPI of 96.093%, 91.709%, and 66.024%, respectively.Correlation coefficients (R) were registered at 92.8%, 98.2%, and 93.3%, while the root mean squared error (RMSE) stood at 0.0969, 0.0604, and 0.2260 respectively.In conclusion, the SVM technique was employed in this study to derive predictive models, yielding superior accuracy for earned value indexes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".