Research on water inrush risk assessment and water inflow chaotic prediction of coastal gold mine
Received:March 07, 2018  Revised:April 29, 2018
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KeyWord:Cloud model assessment; chaos; phase space reconstruction; security early warning; water inflow predictionCloud model assessment; chaos; phase space reconstruction; security early warning; water inflow prediction
  
AuthorInstitution
Feng Lixia 中南大学
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Abstract:
      In view of uncertainty and randomness in the process of risk assessment of gold mine under sea, 10 factors of geological conditions and hydrological conditions were considered to construct evaluation index system, cloud model of water inrush risk assessment was established. Based on the evaluation result, water inrush time series with greater risk were reconstituted in phase space. The evolution laws of distance between two phase points for the water inrush were analyzed and security early warning mechanism of water inrush was established. Combined with phase space reconstruction, an RBF neural network was trained to predict water inflow. The results show that the cloud model judgment of water inrush risk level was in good agreement with the actual situation, system chaotic character was revealed using phase space reconstruction, inner subtle features of water inflow change were amplified with the evolution of distance between two phase points, which provide the basis to the establishment of security early warning mechanism of water inrush, and short-term accurate predictions of water inflow were realized by means of RBF neural network, which provided technical support for underground safety mining.
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