Yan'an gas field is located in the mountainous area of northern Shaanxi Province and the risk of geological disasters along the gas pipeline is high,which has certain safety risks for the normal transportation of the pipeline.The risk prediction can quickly and accurately identify the areas with high consequences,which is of great significance for disaster prevention and mitigation along the pipeline.Therefore,this paper selects Linzhen-Zichang gas transmission line in Yan'an gas field as the research area,selects 11 influencing factors such as elevation,and aspect to study the spatial distribution of disaster points,converts the disaster point attribute value to the weighted frequency value(EM-FR)that can reflect the contribution rate of disaster risks by the weighted frequency ratio method,divides the low and very low risk areas,and chooses non-disaster sites to construct a coupled model of weighted frequency-ratio C5.0 decision tree(EM-FR-C5.0DT)and weighted frequency-ratio BP neural network(EM-FR-BP),predicts the risk of the study area,builds a single C5.0DT and BP model by selecting non-disaster points in the study area,and carries out a precision comparison study with the above coupled models.The results show that the predictive performance of the coupled model is better than that of the single model,EM-FR-C5.0DT has the best effect.It also shows that the coupling model obtained by selecting non-disaster points to build data sets in low and very low risk areas can significantly improve the prediction accuracy of the model,and is more suitable for the risk modeling of small samples of geological disasters,which can provide some reference for the risk research of gas pipeline in Yan'an gas field.
Gas PipelinEntropy MethodC5.0 Decision TreeBP neural networkRisk Prediction
NETL
NSTL
万方数据
