Feature extraction of natural gas leakage for an intelligent warning model: A data-driven analysis and modeling

Abstract

Online monitoring of natural gas in urban underground space has proven to be an effective method for resolving minor leakage and explosion accidents in urban low-pressure natural gas pipelines. However, the harsh environment of urban underground space is often accompanied by the generation of biogas, whose components are more similar to those of natural gas, causing great disturbance to early warning of natural gas leakage. This study correlates the biogas generation principle and its transport law with human activities and environmental changes, analyzes and extracts the characteristics of the natural-gas-biogas CH4 concentration using big data analysis technology, and establishes a natural-gas-biogas sample database. The imbalance between positive and negative samples in the database is addressed by two oversampling techniques. The models trained by two different machine learning algorithms were then evaluated. The results of the study are summarized as follows: (1) There are daily and annual cycles in the CH4 concentration in biogas. The daily trend of biogas in the same manhole is similar, and the biogas in different manholes shows various changes periodically with human activities. Biogas early warnings often occur during the high temperature season (April-September). (2) Features such as period, temperature of alarm, and average concentration over 24 h are positive for improving model accuracy. (3) The combined model of XGBoost and the borderlineSMOTE algorithm has an f-score of 72.7 %, an accuracy of 71.2 %, and a recall of 73.4 %. Compared with the traditional manual classification method, the model proposed in this study can identify natural gas and biogas in a more real-time and accurate manner, reduce the workload of on-site confirmation, and effectively shorten the emergency response time.