引用本文: 武雪玲, 任福, 牛瑞卿, 彭令. 斜坡单元支持下的滑坡易发性评价支持向量机模型[J]. 武汉大学学报 ( 信息科学版), 2013, 38(12): 1499-1503.
WU Xueling, REN Fu, NIU Ruiqing, PENG Ling. Landslide Spatial Prediction Based on SlopeUnits and Support Vector Machines[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12): 1499-1503.
 Citation: WU Xueling, REN Fu, NIU Ruiqing, PENG Ling. Landslide Spatial Prediction Based on SlopeUnits and Support Vector Machines[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12): 1499-1503.

## Landslide Spatial Prediction Based on SlopeUnits and Support Vector Machines

• 摘要: 针对传统滑坡预测手段数据源有限、数据更新周期长、难以发现隐藏在复杂滑坡系统中的规律等问题,本文以三峡库区为研究对象,从多源空间数据中提取滑坡孕灾环境和影响因素等信息,采用数字地形水文分析方法划分斜坡单元,对评价因子进行重采样,进而构建两类支持向量机模型。分析了多源影响因素与滑坡易发性的定量关系,并生成滑坡易发性分区图。采用成功率曲线和误差率评价预测结果,模型预测精度达到98.21%,与野外调查实际情况吻合较好。

Abstract: Landslides are major natural geological disasters in China,and large-scale engi-neering activities induce and aggravate the occurrence of catastrophic landslides.Traditionalspatial analytical techniques cannot easily discover patterns,trends,and relationships thatcan be hidden deep within complicated landslide hazard systems due to limited data sourceand long update cycle.Focusing on the Three Gorges,a variety of environment and trigge-ring factors for landslide occurrence were calculated or extracted from the multi-source spa-tial data.Secondly,the study area was partitioned into slope units derived semi-automatical-ly from a digital elevation model to resample the conditioning factors.Finally,a two-classSVM was trained and then used to map landslide susceptibility with the best accuracy of 98.21%.To evaluate the models,the susceptibility maps were validated by comparing themwith the existing landslide locations according to success rate curve and error rates.

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