摘要
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。
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.
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2009年第8期126-132,共7页
China Safety Science Journal
基金
"十一五"国家科技支撑计划项目(2006BAB02A02)
湖南省安全生产科技发展计划项目(07-17
07-29
HN08-07)
湖南省教育厅资助项目(07C652
08B068)
关键词
矿山地质环境
评价模型
粗糙集
BP神经网络
评价指标
mining geological environment
evaluation model
rough set
BP neural network
assessment indicator
