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Geological Hazards Assessment in Taibai by the Case of Land-slide

Author: JiaShaoJie
Tutor: YangMeiZhong
School: Xi'an University of Science and Technology
Course: Geological Engineering
Keywords: Geological Hazards Hazard evaluation BP neural network SVM
CLC: P694
Type: Master's thesis
Year: 2011
Downloads: 71
Quote: 2
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Abstract


Taibai more serious geological disasters, the disaster point of many types and dist-ribution into the film into a zone, quite different scale, impact and controlled by man-y factors, high frequency, small and medium-sized disaster points, which are significant to level against disaster points less; because of its complex geological conditions, ter-rain conditions, meteorological and hydrological conditions and a strong new tectonic movement and human engineering activities, making the area into one of geological disasters counties.In this paper, 2009 "Shaanxi Taibai detailed investigation of geological disasters, " the task of Taibai geological disasters such as a detailed investigation and laboratory test based on the work, summed up the landslide hazard area and development characteristics of point distribution; and the region Point landslide risk assessment carried out to study the main contents are:(1) Analysis of the regional landslide hazard point type, size, development characteristics and formation conditions; landslide hazards in the analysis of survey data point-s based on the study, according to the characteristics of regional landslide hazard poin-ts to the scale, stability , the number of threats, threats to property, restricting the nu-mber of four risk factors AHP construct the training sample set.(2) application of MATLAB toolbox, called newff () function of neural networks, BP algorithm with momentum SCG training algorithm results were compared to deter-mine the SCG algorithm for training methods.(3) application LibSVM toolbox to implement support vector machine model, by t-he polynomial kernel function, radial basis function RBF and Sigmoid kernel function of the sample training results and error analysis to determine the radial basis kernel function RBF as a model.Application of two methods in the study area 81 point landslide hazard risk inde-x, and the results were analyzed and compared with the previous qualitative assessment of risk of hidden point was consistent, the results of this study point to Taibai risks of geological disasters prevention and artificial intelligence Applied to geological disa-sters have hidden point risk assessment guidance.

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CLC: > Astronomy,Earth Sciences > Geology > Disaster geology
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