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

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
计量
- 文章访问数: 130
- HTML全文浏览量: 12
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2016-12-18
- 发布日期: 2017-12-25

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

摘要

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。
- 滑坡易发性评价 /
- 因子筛选 /
- 粗糙集 /
- 遗传算法 /
- BP 神经网络
Abstract: Landslide susceptibility evaluation is critical for landslide medium and long-term prevention and territorial planning. In order to improve the precision and accuracy of evaluation result, the study was carried out in Longfeng Town of Enshi City. Firstly, the Geographic Information System (GIS) was suggested to use as the basic tool for spatial data management, 13 initial evaluation factors were selected including lithology, slope angle, distance to geological structures etc. Then the rough set theory based on genetic algorithm was used to reduce the redundant information of 13 initial factors in the decision table and determine the kernel including 8 representative factors, namely, lithology, altitude, curvature, distance to roads, distance to river, slope angle, aspect, stream power index. After that, the kernel factors were used to train a BP neural network model, and landslide susceptibility index (LSI) and landslide susceptibility classification map were achieved. The highest susceptibility zone is about 12.82% of the total area, including 78.11% landslide-prone area. The ROC curve test result shows that the prediction accuracy of the RS-BPNN model is about 90.9%, proving that the RS-BPNN model has advantages of excellent prediction performance and efficiency and has higher practical value.
- landslide susceptibility evaluation /
- factor selection /
- rough set theory /
- genetic algorithm /
- back-propagation neural network

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参考文献(32)

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