Data analysis and modeling of gas pipeline intelligent management system based on machine learning algorithm

The management of gas pipelines requires efficient, predictive systems to enhance safety and operational efficiency. The limitations include potential data quality issues, sensor accuracy, and environmental factors that may affect model performance. To develop an intelligent gas pipeline management system by integrating the Wombat Algorithm-driven Scalable Random Forest (WA-SRF) for improvement of predictive accuracy, scalability, and fault detection in the performance of operations for gas pipelines, various sensors are embedded in the pipeline, and data collected from these sensors captures the real-time metrics, which include pressure, flow rates, and temperature. Data is preprocessed using Z-score normalization (ZSN), ensuring corrected differences in sensor values by maintaining consistent input values. Feature extraction is done with Fast Fourier Transform (FFT), in which time-domain data gets converted into frequency-domain features. The WA-SRF model is implemented with Python and shows enhanced accuracy of prediction for pipeline failures and anomalies. The model has achieved an accuracy of 98.7% in failure prediction and anomaly detection, highlighting its potential for real-time applications in pipeline management. The proposed method enhances predictive accuracy for anomaly detection, achieving an AUC of 0.98, and gives scale to maintenance optimization with a guarantee of overall safety and efficacy in gas pipeline operations.