From Forecasting to Prevention: Operationalizing Spatiotemporal Risk Decoupling in Gas Pipelines via Integrated Time-Series and Pattern Mining
Year: 2025
Type: article
Abstract: Accurate prediction of gas pipeline incidents through risk factor interdependencies is critical for proactive safety management. This study develops a hybrid SARIMA-Association Rule Mining (ARM) framework integrating time-series forecasting with causal pattern decoding, using 60-month U.S. pipeline incident records (2010–2024) covering leaks, mechanical damage, and ruptures. Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling with six-month rolling-window validation achieves precise leak forecasts (MAPE=14.13%, MASE=0.27) and reasonable mechanical damage predictions (MAPE=31.21%, MASE=1.15), while ruptures exhibit pronounced stochasticity. Crucially, SARIMA incident probabilities feed Apriori-based ARM, revealing three failure-specific mechanisms: (1) ruptures predominantly originate from natural force damage, with underground cases causing economic losses (lift=3.70) and aboveground class 3 incidents exhibiting winter daytime ignition risks (lift=2.37); (2) leaks correlate with equipment degradation, where outdoor meter assemblies account for 69.7% of fire-triggering cases (108/155 incidents) and corrosion dominates >50-year-old pipelines; (3) mechanical damage clusters in pipelines <20 years during spring excavation, predominantly occurring in class 2 zones due to heightened construction activity. These findings necessitate cause-specific maintenance protocols integrating material degradation laws and dynamic failure patterns, providing a decision framework for pipe replacement prioritization and seasonal monitoring in high-risk zones.
Source: Processes
DOI: 10.3390/pr13113589
Author: shengli liu
Institution: Hebei Normal University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415988428