Predicting Environmental Impact of Hazardous Liquid Pipeline Accidents: Application of Intelligent Systems

Abstract

In case of failure, hazardous liquid pipelines can have adverse environmental consequences. This study presents a method to predict the occurrence of certain environmental impacts resulting from hazardous liquid pipeline accidents. Explanatory variables, including pipe diameter, commodity transported, and incident area type, are used to train an adaptive neuro-fuzzy inference system (ANFIS). Three impact types are analyzed: water contamination, soil contamination, and impact on wildlife. Results show that the model can accurately predict whether a pipeline segment with given design characteristics could lead to adverse environmental impacts due to failure (14%, 6%, and 3% error for soil and water contamination and impact on wildlife, respectively). This model can be used in pipeline design and risk management planning to minimize the potential for environmental consequences. However, more comprehensive and robust reporting requirements beyond simple occurrence would improve our ability to prioritize these mitigative actions.