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BP神经网络和SVM在矿山环境评价中的应用分析/Application of BP neural network and SVM in mine environmental assessment[J]
Authors
LI Dong
LIU Hui
+13 more
SUN Wei-dong
WANG Jin-lin
YU Hao
ZHOU Ke-fa
中国科学院大学,北京100049
中国科学院新疆生态与地理研究所,新疆矿产资源研究中心,新疆乌鲁木齐830011
于浩
刘慧
周可法
孙卫东
新疆维吾尔自治区地质矿产勘查开发局信息中心,新疆乌鲁木齐830000
李东
王金林
Publication date
1 January 2015
Publisher
Abstract
矿山环境的影响因素多样,定量评价过程易受人为因素干预.BP神经网络与SVM算法能够自动模拟各因子间的非线性关系.首次将其引入到矿山环境评价中,选取160个单元作为训练样本,以自然地理、基础地质、开发占地及地质环境等4个大类的14个变量指标为输入向量,以单元评价得分为输出向量,分别建立BP神经网络与SVM矿山环境评价模型.结果表明:两种模型均能满足矿山环境评价的精度要求;SVM模型收敛速度较BP神经网络快,MSE小于BP神经网络,更适合矿山环境评价工作;将定量模型应用于研究区,评价得分划分为4个级别,与定性评价结果一致,为矿山环境评价工作提供了新思路
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Last time updated on 29/11/2016