Research On The Key Technologies Of Forecasting Decision Support System Of Mine Water Inrush

The coal mine water inrush seriously threat to the miners' personal safety, andthe national significant economic losses. Forecasting water burst the decision-makingand more subjective analysis of the basis for decision-makers for the problem, thisdecision-making has some limitations. The one hand, the decision makers'understanding of the situation may not be clear, comprehensive know-how may not be,on the other hand the the water inrush is one of uncertainty, nonlinear complexprobability event.This study is in support of the computer about how to make decisions for waterinrush prediction, taking into account with the probability of water inrush locationsimilar water inrush. The study would have taken place in a typical case of waterinrush data as a starting point, from known to unknown, using of past cases orexperience to carry out the reasoning used to solve new problems.In this paper, research results are as follows:(1) The coal mine water inrush typical case of database is designed, includingdesign of the conceptual structure and logical structure of the case database, usingMicrosoft SQL Server relational database management system.225cases arecollected. Coal mining history of water inrush data is converted to digital data;(2) The three-dimensional spatial data model is proposed for the minehydrogeological body based on the3D data model of the hybrid structure, and thedata structure of data models are designed, including TEN, TIN and Octree;(3) The use of parallel coordinates visualization of water inrush data and Iris datafrom UCI, on the basis of standardization, transformation, translation data, drawinga parallel plot, and the existence of sensitive attributes is found;(4) Based on fuzzy adaptive neural network structure and learning algorithms,the selection of standardized methods, choice of input attributes, the choice ofmembership function for comparative analysis, building ANFIS model for waterinrush prediction;(5) Research support vector machine model for mathematical reasoning processto build the SVM model for the inrush from floor predicting water optimizationselection algorithm and cross-validation algorithm proposed parameters C and γ;(6) The water inrush prediction knowledge base based on ontology is designed,including water inrush ontology and rule base of water inrush, and a formal definitionof fuzzy rule is made.