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• W3114020123 abstract "The transportation of natural gas often relies on pipelines which require constant monitoring and regular maintenance to prevent spills or leaks. Pipeline incidents could pose a huge adverse impact on people, the environment, and society. Numerous efforts have been invested to identify contributing factors to pipeline incidents so that countermeasures could be developed to proactively prevent some incidents and reduce incident severities or impacts. However, the countermeasures may need to vary for different incidents due to the potential heterogeneity between incidents, and such heterogeneity is likely related to the geology, weather, and built environment which vary across space and time domain. The objective of this study is to revisit the correlates of pipeline incidents, focusing on the spatial and temporal patterns of the correlations between natural gas pipeline incident severity and contributing factors. This study leveraged an integrated spatio-temporal modeling approach, namely the Geographically and Temporally Weighted Ordered Logistic Regression (GTWOLR) to model the natural gas pipeline incident report data (2010–2019) from the U.S. Pipeline and Hazardous Material Safety Administration. Text mining was performed to extract additional information from the narratives in reports. Results show several factors have significant spatiotemporally varying correlations with the pipeline incident severity, and these factors include excavation damage, gas explosion, iron pipes, longer incident response time, and longer pipe lifetime. Findings from this study are valuable for pipeline operators, end-users, responders to jointly develop localized strategies to maintain the natural gas distribution system. More implications are discussed in the paper." @default.
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• W3114020123 date "2021-03-01" @default.
• W3114020123 modified "2023-10-16" @default.
• W3114020123 title "Severity of emergency natural gas distribution pipeline incidents: Application of an integrated spatio-temporal approach fused with text mining" @default.
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• W3114020123 doi "https://doi.org/10.1016/j.jlp.2020.104383" @default.
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