Abstract
The SCADA system plays an important role in monitoring the long distance operation of mass pipeline network, which may experience huge damage due to landslides geological hazards. It is critical to detect the deformation and displacement of rock to forecast the damage of landslides geological hazards through analyzing detailed information collected by SCADA system. In this paper, we use advanced TDR real-time technology to monitor the factors of rock’s inclination, displacement, and humidity, and take advantage of factor neural network (FNN) theory to build a simulation-type factor neural network model. Particularly, based on FNN model, we design an expert system to forecast the potential risks of geological disasters through analyzing the real-time information of the large-scale network in the SCADA system.
This paper is supported by National Natural Science Foundation of China. (Grant No. 61175122).
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Cao, X., Wei, C., Li, J., Yang, L., Zhang, D., Tang, G. (2012). The Geological Disasters Defense Expert System of the Massive Pipeline Network SCADA System Based on FNN. In: Wang, H., et al. Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29426-6_4
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DOI: https://doi.org/10.1007/978-3-642-29426-6_4
Publisher Name: Springer, Berlin, Heidelberg
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