A Time Series-based Data Modelling Approach for Natural Gas Pipeline Intelligent Management System
DOI:
https://doi.org/10.15837/ijccc.2025.4.6813Keywords:
Time series analysis, Gas pipeline, Long short-term memory network, Attention mechanism, Data managementAbstract
The study proposes an integrated method for intelligent management and risk prediction of natural gas pipelines using time series analysis. The method combines k-nearest neighbor imputation for data preprocessing, long short-term memory networks, and an attention mechanism for temporal data prediction to introduce a new intelligent data management model. The research method is tested by testing real pipeline data from eight scenarios in the gas pipeline operation monitoring dataset and the gas pipeline failure dataset. The experimental results demonstrated a notable enhancement in performance relative to existing methods, with data coverage reaching up to 92%, a classification error rate of 5%, an accuracy in risk prediction of 95%, and a processing time of 12.7 seconds. The framework provides a comprehensive solution for improving the safety, efficiency, and reliability of natural gas pipeline operations through advanced data analytics.
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