Open AccessModel building and simulation for intelligent early warning of long-distance oil & gas storage and transportation pipelines based on the probabilistic neural network
Author(s) -
Weiwei Kong,
Jianwen Yu,
Jia Yang,
Tao Tian
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/546/2/022009
Subject(s) - artificial neural network , probabilistic logic , warning system , probabilistic neural network , pipeline transport , backpropagation , computer science , hazard , pipeline (software) , early warning system , process (computing) , engineering , artificial intelligence , data mining , time delay neural network , telecommunications , programming language , chemistry , organic chemistry , environmental engineering , operating system
In order to solve the potential safety hazard of long-distance oil and gas pipelines, an intelligent early morning warning model is constructed and simulated based on probabilistic neural network in this paper. The backpropagation (BP) networks and the probabilistic neural networks (PNNs) are used to process the collected abnormal data and build the early warning model. The early warning model is simulated for its accuracy in the computer and its feasibility is verified. The intelligent early warning model is built, preparing the groundwork for the subsequent generalization and application.