基于粗糙集-神经网络的矿山地质环境影响评价模型及应用

采用衡山白果地区石膏矿山的11个评价指标,综合运用粗糙集和神经网络理论,构建了基于粗糙集-神经网络(RS-ANN)的矿山地质环境影响评价模型,对RSES软件约简的数据和无约简的数据采用EasyNN-plus软件进行预测评价。神经网络模型的输入属性为8个,而粗糙集-神经网络模型的输入属性为6个,训练样本均为13个,预测样本均为4个,前者的平均预测精度为1.85%～24.86%,后者为1.23%～15.28%。研究发现,粗糙集在保留关键信息的前提下可有效地对数据表进行约简,约简后的神经网络预测结果与实际情况吻合,并比无约简时总体精度有较大幅度提高。

Study on the Model of Mines' Geological Environmental Impact Assessment Based on Rough Set and Artificial Neural Network and Its Application

Through referring to the 11 assessment indicators of gypsum mines in Baiguo region of Hengshan County,a model for mines' geological environmental impact assessment is set up based on rough set(RS) and artificial neural network(ANN).Then,through adopting EasyNN-plus software,a prediction evaluation is made on the raw data and the data reduced by RSES software.The input attributes of the ANN model are 8,the RS-ANN model input attributes are 6,both training samples are 13,both forecast samples are 4,the former average prediction accuracy is 1.85%-24.86%,the latter is 1.23%-15.28%.This study shows that rough set is effective in the data table reduction while retaining key information;the results predicted by RS-ANN model coincide with the actual situation,and the overall accuracy greatly rises.