An Analysis of Machine Learning Solutions for Precise Forecasting of Oil and Gas Pipeline

Background: The integrity and dependability of pipelines carrying oil and gas depend on the precise identification of corrosion and leakage. There is a lack of scalability, consistent accuracy, and real-time prediction in traditional inspection techniques including sensor-based systems and human evaluations. Methods: This research explores an application of machine learning (ML) techniques to improve leak detection. The Oil-and-Gas Pipeline Leakage dataset, with 10,293 instances and eight numerical features, is used. The regression problem is converted to a classification task using label binarization. Preprocessing includes data cleaning, min-max normalization, and feature extraction. ML models—DT, RF, and proposed SVM are trained and evaluated. Results: The SVM model achieved the highest performance with 96.1% accuracy, 96% precision, 96% recall, and a 97% F1score, outperforming DT and RF. Conclusion: The study demonstrates that ML models, particularly SVM, can effectively predict pipeline leaks. These findings support integrating ML into predictive maintenance for dynamic and reliable pipeline monitoring.