Design And Realization Of Disaster Evaluation And Early Warning System In Mine Area Based On GIS

With the development of industrial society, the demand of energy resource which is the driving force of economy and development in the world is in a growing trend. As the main energy resource in our country, the demand of coal leads to much more coal mining. As a result, the amount of the geological disasters happens in the mining area increase by degrees. In order to avoid the damage which suffers in the process of coal mining, ensure the safety of surrounding residents and reduce the property damage and casualties in disaster, it is necessary to provide a powerful forecast for the geological disasters. The main types of disaster in mining area are landslide, subsidence, collapse, ground fissure., the most influential factors of these disasters in the mine environment are slope degree, aspect, distance to geologic tectonic line, distance to minery, distance to road, lithology, roughness, and DEM. The impact of various factors are on different levels for different disaster and diverse interaction between the various factors in different spatial location and mining environmental conditions. So for the evaluation and early warning of geological hazards, we need to use the spatial data of various influential factors and statistical analysis models. The integration of spatial data and statistical analysis models can take full advantage of the location features of spatial data and the scientificity to get an accurate,intuitive, clear assessment early warning result.The paper relies on insight of mechanism in occurrence of geological disasters, investigation and research on disasters occurred in the mining area, uses GIS technology and statistical analysis models to design and implement of early warning systems for geological hazard assessment. Spatial data could be processed in GIS using analysis and storage tools, with location, time and space to get more accurate forecasts and more intuitive expression. The statistical analysis models such as neural network, hierarchical analysis, logistic regression which have been used much in decision analysis applications can change the traditional decision-making method with subjective decision-makers, achieve the decision of forecast, analysis, evaluation and program by scientific analysis.The combination of GIS and statistic models can provide more agile application of spatial data. According to the changes of data, the evaluation model fit with the study area will be set up to get an accurate visual evaluation results. More powerful analysis and decision functions could provide decision basis with spatial feature, intuition, objectivity.