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基于粗糙集和BP神经网络的滑坡易发性评价

唐睿旋 晏鄂川 唐薇

唐睿旋, 晏鄂川, 唐薇. 基于粗糙集和BP神经网络的滑坡易发性评价[J]. 煤田地质与勘探, 2017, 45(6): 129-138. doi: 10.3969/j.issn.1001-1986.2017.06.021
引用本文: 唐睿旋, 晏鄂川, 唐薇. 基于粗糙集和BP神经网络的滑坡易发性评价[J]. 煤田地质与勘探, 2017, 45(6): 129-138. doi: 10.3969/j.issn.1001-1986.2017.06.021
TANG Ruixuan, YAN Echuan, TANG Wei. Landslide susceptibility evaluation based on rough set and back-propagation neural network[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(6): 129-138. doi: 10.3969/j.issn.1001-1986.2017.06.021
Citation: TANG Ruixuan, YAN Echuan, TANG Wei. Landslide susceptibility evaluation based on rough set and back-propagation neural network[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(6): 129-138. doi: 10.3969/j.issn.1001-1986.2017.06.021

基于粗糙集和BP神经网络的滑坡易发性评价

doi: 10.3969/j.issn.1001-1986.2017.06.021
详细信息
    第一作者:

    唐睿旋(1989-),女,贵州遵义人,博士,研究方向为斜坡地质灾害及工程岩体稳定性.E-mail:arzkama23@126.com

  • 中图分类号: P642.22

Landslide susceptibility evaluation based on rough set and back-propagation neural network

  • 摘要: 区域滑坡易发性评价是国土规划和滑坡中长期防治的重要依据。为进一步提高滑坡易发性评价的准确性,以恩施市龙凤镇为研究区,运用地理信息系统GIS技术,获取了包括工程岩组、坡度、地质构造等在内的13个初始评价因子,利用基于遗传约简算法的粗糙集理论对初始评价因子进行属性约简,去掉冗余属性后获得最小约简,即8个核评价因子:工程岩组、高程、地形曲率、道路、水系、坡度、坡向、径流强度指数,并以此作为BP神经网络的输入层,构建RS-BPNN预测模型,获得滑坡易发性指数LSI及滑坡易发性等级分区图。其中高易发区面积占总面积的12.82%,该区包含的滑坡面积占总滑坡面积的78.11%,通过ROC曲线测试,模型预测精度为90.9%。结果表明,RS-BPNN模型预测性能良好,进一步提高了滑坡易发性评价的精度和准确性,有较高的工程实用价值。

     

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出版历程
  • 收稿日期:  2016-12-18
  • 刊出日期:  2017-12-25

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