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Risk Assessment for Mine Debris Flow Based on Genetic Algorithm & Artificial Neural Network

Author: WangGuoFeng
Tutor: XueXiCheng
School: Xi'an University of Science and Technology
Course: Environmental Engineering
Keywords: Mine Debris Flow Risk assessment Genetic Algorithm Artificial Neural Network
CLC: P642.23
Type: Master's thesis
Year: 2008
Downloads: 330
Quote: 3
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Abstract


Mining industry is an important basic industry in our country. Since the reform and opening up, many domestic areas rely on the mineral resource superiority to develop the mining industry economy positively, and have promoted the local economy fast development. However, high strength mineral resource development actually is initiating geological disasters unceasingly , such as avalanche, landslide and debris flow . It has seriously damaged the ecological environment of the mining area . Especially the mine debris flow disaster which take mining dregs as the principal matter source, it is threatening the mine enterprise and the life and property security of the resident in mountainous area frequently . For effectively prevention and control and government mine debris flow disaster, and promotion mine safety production and social harmonious development, it has the important theory and practical significance to develop the synthetic risk assessment work for mine debris flow scientifically.The paper takes the lead-zinc mining area in Shanxi Feng County as an example.Based on the systematical investigation in field, the paper has carried on detailed analysis to the formation conditions and distribution rules of mine debris flow. Secondly, the paper has established an indicator system to assessment the risk of mine debris flow and finished the quantification of all the assessment indicators, the paper has used the grey relational analysis method to realize optimized screening the indicator system and obtained the main controlling factors which is the influence of mine debris flow.According to the limitation of the present assessment mathematical models, the author combined with the artificial neural networks and the immunity genetic algorithm, proposed the nonlinear mathematical model to synthetic risk assessment for mine debris flow . With the aid of object-oriented programming tool Visual Basic 6.0 and Access 2000 database technology , the Mine Debris Flow Risk Synthetic Assessment System is developed and used to realize the synthetic risk assessment for mine debris flow. It has been confirmed that the software is accuracy and reliability. It has raised the efficiency and accurate degree of the mine debris flow risk assessment exercise.

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CLC: > Astronomy,Earth Sciences > Geology > Hydrogeology and Engineering Geology > Engineering Geology > The geological and engineering geological > Debris flow
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