基于GIS和人工神经网络技术的开采沉陷预计建模方法

探讨了GIS支持下利用人工神经网络技术进行开采沉陷定量预测的方法和思路，完成了影响因素的选取、数据处理、开采沉陷初始预计模型的建立及验证。数据处理由GIS软件完成，采用BP神经网络训练方法对开采沉陷系统进行建模；进而结合验证结果，对误差进行了定性分析。研究结果表明，利用GIS支持下的神经网络模型对复杂的开采沉陷系统进行模拟预测，具有理论上的可行性和现实意义。说明GIS和人工神经网络技术在开采沉陷预计领域中具有广阔应用前景。

Prediction Model of Exploitation Sink Using Ann and GIS

A new idea is put forward on researches of prediction method of coal mining subsidence supported by GIS, and a new method is given about quantitative prediction of mining subsidence by means of GIS and ANN(Artificial Neural Network).This paper has completed a lot of work concerning choosing factor,data processing, establishing and validating preliminary ANN prediction model. Further more,data processing is carried out by GIS software,and the BP training method is used for modeling the exploitation sink system. Subsequently,the error is qualitatively analyzed with considering the result of verification. Researches show that the ANN prediction model supported by GIS has theoretical feasibility and realistic significance in predicting complex exploitation sink system,and GIS and ANN possess wide application prospects in the prediction of exploitation sink.