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中南大学学报(自然科学版)

Journal of Central South University

第46卷    第6期    总第250期    2015年6月

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文章编号:1672-7207(2015)06-2324-09
基于粗糙集和粒子群优化支持向量机的滑坡变形预测
赵艳南1,牛瑞卿1,彭令2,程温鸣3, 4

(1. 中国地质大学(武汉) 地球物理与空间信息学院,湖北 武汉,430074;
2. 中国地质环境监测院,北京,100081;
3. 中国地质大学(武汉) 工程学院,湖北 武汉,430074;
4. 三峡库区地质灾害防治工作指挥部,湖北 宜昌,443000
)

摘 要: 以三峡库区白水河滑坡为例,首先分析降雨量与库水位等影响因素与滑坡变形特征的响应关系,然后利用粗糙集理论对10个初始影响因子进行属性约减,筛选出影响滑坡变形的核因子集,最后基于该因子集建立粒子群优化支持向量回归模型,对滑坡位移速率进行预测。研究结果表明:测试样本的预测结果与实测值变化趋势基本一致,其平均绝对误差为0.234 mm/d,均方差和判定系数分别为0.163和0.520。粗糙集理论在分析滑坡变形特征、筛选关键因子方面的适用性与科学性,构建的粗糙集-粒子群优化支持向量机模型具有较高的泛化能力,是一种有效的滑坡变形预测方法。

 

关键字: 滑坡变形预测;粗糙集;粒子群优化;支持向量机

Prediction of landslide deformation based on rough sets and particle swarm optimization-support vector machine
ZHAO Yannan1, NIU Ruiqing1, PENG Ling2, CHENG Wenming3, 4

1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China;
2. China Geological Environment Monitoring Institute, Beijing 100081, China;
3. Engineering Faculty, China University of Geosciences, Wuhan 430074, China;
4. Command of Geological Hazard Control in Area of Three Gorges Reservoir, Yichang 443000, China

Abstract:The Baishuihe landslide in the Three Gorges Reservoir region was selected as an example. By analysing the response relationships between landslide deformation and influencing factors such as the rainfall and the reservoir water level, 10 initial influencing factors were reduced by using the rough set theory(RS). Then, the nuclear factor set influencing the landslide deformation was screened out. Finally, the particle swarm optimization (PSO) - support vector regression (SVR) model was established based on the nuclear factor set to predict landslide displacement rate. The results show that the test sample predictive mean absolute error, mean squared error and determination coefficient are 0.234 mm/d, 0.163 and 0.520, respectively. And the change trends are consistent between predicted results and the measured ones. The rough set theory is scientific and applicable in analysing landslide deformation characteristics and selecting key factors. The RS-PSO-SVR model is an effective method in landslide deformation predicting with high generalization ability.

 

Key words: landslide deformation prediction; rough sets; particle swarm optimization; support vector machine

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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