Integrative Hybrid Machine Learning Methodologies for Decoding and Analysing Complex Encoded Data on Oil Pipeline Incident Patterns and Risks

Oil pipelines are vital components of global energy infrastructure, but it is susceptible to accidents that leads to severe environmental and economic consequences. Accidents are often preceded by complex, high-dimensional sensor data, which is challenging to interpret and analyze directly. This paper presents a novel hybrid Machine Learning (ML) approach, called the Hybrid Boost Forest Net, for analyzing encoded data on oil pipeline accidents. Boosting is a popular ensemble learning method that improves predictive accuracy by sequentially correcting the errors of weak models. ForestNet, on the other hand, leverages a deep neural network architecture specifically designed to handle complex, high-dimensional data through multiple layers of feature extraction. In this hybrid approach, Boosting first refines the data by focusing on difficult- to-predict accident instances, while Forest Net further processes the refined data, identifying intricate patterns and dependencies between pipeline conditions, environmental factors, and operational parameters. The method is tested using real-world accident data, demonstrating superior performance in accident prediction compared to traditional machine learning models. Results indicate that the Hybrid Boost ForestNet approach not only enhances predictive accuracy but also improves interpretability, offering significant potential for early detection, risk assessment, and preventive measures in pipeline safety management. The evaluation phase employs metrics like mean squared error (MSE) and mean absolute error (MAE) to assess the model’s predictive performance. This project is implemented using Python, achieving a MSE of 0.71 and an accuracy of 96%.