Developments and applications of the self-organizing map and related algorithms
Mathematics and Computers in Simulation - Special issue: signal processing and neural networks
Self-organizing neural projections
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Defect spatial pattern recognition using a hybrid SOM-SVM approach in semiconductor manufacturing
Expert Systems with Applications: An International Journal
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Accidents to pipelines because of the third-party interference have been recorded and they often result in catastrophic consequences for environment and society with a great deal of economic loss. The third-party interference resulting from complicated origins occurs randomly, and is hard to be forecasted or controlled in advance, so it becomes a serious threat to the safe operation of long transmission pipeline. This paper focuses on the application of self-organizing maps (SOMs) to assess the risk of third-party interference and classify their risk patterns. In this work, fault tree is used first to establish the risk assessment index system, and then SOM is used in multi-parameter risk pattern classification approach, which is proposed to present various risk maps, incorporating the factors of pipeline laying conditions, historical damage records, safety-related actions, management measures and the environment around the underling pipeline. A field case study of Shaanxi-Beijing gas pipeline in China is undertaken so that the effectiveness of the proposed approach could be verified. By taking the classification results into consideration, the decision maker may well get precious and differentiated information about the pipeline risk distribution of third-party interference and make appropriate safety-related actions to prevent the damage.