Prediction of Leak on Gas Pipeline Using a Hybrid Machine Learning Model

DOI: 10.14800/IOGR.1344

Abstract

Leak detection is an important problem during transportation of natural gas through pipelines for downstream operations. The investigation into solving this problem has led to the adoption of data science and machine learning approaches providing an optimal solution to this problem. In this study, a machine learning hybrid model was developed to detect natural gas leakages in pipelines. The hybrid model referred to as voting regressor assembles two machine learning models, the Random Forest Regressor and the XGBoost Regressor for improving performance during leak detection. The input parameters to the hybrid model are temperature, pressure, and flow rate. Results from this study showed an improvement in the performance of the hybrid model (voting regressor) in comparison to others in leak detection. This is depicted by an accuracy score of 93% and an error of 0.44%, and a recommended approach for leak detection.