GIS支持下应用PSO-SVM模型预测滑坡易发性

武雪玲, 沈少青, 牛瑞卿

武雪玲, 沈少青, 牛瑞卿. GIS支持下应用PSO-SVM模型预测滑坡易发性[J]. 武汉大学学报 ( 信息科学版), 2016, 41(5): 665-671. DOI: 10.13203/j.whugis20130566
引用本文: 武雪玲, 沈少青, 牛瑞卿. GIS支持下应用PSO-SVM模型预测滑坡易发性[J]. 武汉大学学报 ( 信息科学版), 2016, 41(5): 665-671. DOI: 10.13203/j.whugis20130566
shu
WU Xueling, SHEN Shaoqing, NIU Ruiqing. Landslide Susceptibility Prediction Using GIS and PSO-SVM[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 665-671. DOI: 10.13203/j.whugis20130566
Citation: WU Xueling, SHEN Shaoqing, NIU Ruiqing. Landslide Susceptibility Prediction Using GIS and PSO-SVM[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 665-671. DOI: 10.13203/j.whugis20130566
shu

GIS支持下应用PSO-SVM模型预测滑坡易发性

  • 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.

    国土资源部城市土地资源监测与仿真重点实验室, 广东深圳, 518034

基金项目: 国家自然科学基金(41501470);国土资源部城市土地资源监测与仿真重点实验室开放基金(KF-2015-01-006);资源与环境信息系统国家重点实验室开放基金。
详细信息
    作者简介:

    武雪玲,博士,副教授,现主要从事滑坡灾害预测预报研究。snowforesting@163.com

    通讯作者:

    沈少青,博士,工程师。s_s_q@126.com

  • 中图分类号: P208;P237.9

Landslide Susceptibility Prediction Using GIS and PSO-SVM

  • 1.

    Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China;

  • 2.

    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China

Funds: The National Natural Science Foundation of China, No.41501470; Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, the Ministry of Land and Resources, No.KF-2015-01-006; State Key Laboratory of Resources and Environmental Information System.

摘要

    摘要
  • 摘要: 滑坡灾害易发性预测是滑坡监测、预警与评估的关键技术。如何有效地选取评价因子和构建预测模型是滑坡灾害定量预测研究中的难题。本文以三峡库区长江干流岸坡作为研究区,通过地形、地质和遥感等多源数据融合,提取滑坡孕灾环境和诱发因素的信息作为评价因子。在此基础上,针对滑坡灾害的非线性和不确定性特征,采用粒子群算法对支持向量机模型参数进行全局寻优,构建粒子群算法(particle swarm optimization, PSO)-支持向量机(support vector machine, SVM)模型,定量预测滑坡易发性。最后通过分类精度比较分析基于格网单元和对象单元的滑坡易发性预测精度,结果表明,基于对象单元的PSO-SVM预测精度较高,其曲线下面积为0.841 5,Kappa系数为0.849 0,预测结果与野外实际调查情况较为一致,可为三峡库区滑坡防灾减灾工作提供参考。
    Abstract: Landslide susceptibility prediction is the key technology in landslide monitoring, early warning, and assessment. The core problem in quantitative prediction of landslide hazards is the effective selection of conditioning factors and prediction models. In this paper, the Three Gorges Reservoir area was selected as a case study to predict landslide susceptibility. First, key landslide-related factors were selected as input variables using topographic, geological, and remote sensing data. Secondly, according to the nonlinear and uncertainty characteristics of landslides, a PSO-SVM model was trained and used to assess landslide susceptibility. Finally, the prediction results of grid-and object-based prediction models were validated by comparing them with known landslides using the classification accuracy. The results show that object-based PSO-SVM possesses high prediction accuracy with the area under curve of 0.841 5 and a Kappa coefficient of 0.849 0. These experimental results are consistent with field investigations and can provide a reference for landslide prevention and reduction in the Three Gorges, China.
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    • 参考文献(20)
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出版历程
  • 收稿日期:  2015-08-24
  • 发布日期:  2016-05-04

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