首页> 外文期刊>Canadian Geotechnical Journal >Regional-scale landslide susceptibility mapping using the weights of evidence method: An example applied to linear infrastructure
【24h】

Regional-scale landslide susceptibility mapping using the weights of evidence method: An example applied to linear infrastructure

机译:使用证据权重法的区域尺度滑坡敏感性地图:应用于线性基础设施的示例

获取原文
获取原文并翻译 | 示例
           

摘要

Large landslides are common in the gently sloping clay plains of the Saint Lawrence Lowlands of eastern Canada. These tend to occur along rivers carved into the marine soils deposited in the former Champlain Sea, which occupied the area roughly 10000 years ago. This paper presents a landslide susceptibility model, developed at the regional scale using a bivariate statistical method: the weights of evidence method. The analysis considers the association of existing large landslides in a portion of the study area with key terrain features, such as ground elevation, flow accumulation in adjacent streams, soil type, soil thickness, and land use. The resulting model identifies three different levels of susceptibility: low, low to moderate, and moderate to high. These descriptors are related statistically to the probability of encountering existing large landslides within 500 m, 1 or 2 km, respectively. The model is tested along primary railway corridors and isolates 8% of the total length for further consideration of landslide hazard. Reconnaissance level air photo survey results further reduce the length of corridor with elevated susceptibility to 2% of the total length, thus focusing the application of additional resources to a very small proportion of the total inventory.
机译:在加拿大东部圣劳伦斯低地平缓的粘土平原上,大的滑坡很常见。这些往往发生在雕刻在约一万年前占领该地区的前尚普兰海的海洋土壤中的河流上。本文介绍了一种滑坡敏感性模型,该模型在区域范围内使用双变量统计方法:证据权重方法开发。该分析考虑了研究区域一部分中现有的大型滑坡与关键地形特征的关联,例如地面高程,相邻溪流中的流量累积,土壤类型,土壤厚度和土地利用。结果模型确定了三种不同的磁化率:低,低至中等以及中至高。这些描述符在统计学上分别与遇到500 m,1 km或2 km之内的现有大型滑坡的概率有关。该模型在主要铁路走廊沿线进行了测试,并隔离了全长的8%,以进一步考虑滑坡灾害。侦察级空中照相调查结果进一步将易感性提高的走廊的长度减少到总长度的2%,从而将额外资源的应用集中在总清单的很小一部分上。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号