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Hazard and population vulnerability analysis: a step towards landslide risk assessment

机译:危害与人口脆弱性分析:迈向滑坡风险评估的一步

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In this paper,an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlán,Puebla,Mexico,is presented.In order to estimate landslide hazard,the susceptibility,magnitude (area-velocity ratio) and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory;the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources (Google Earth,aerial photographs and historical information).Estimations of landslide susceptibility were determined by combining four statistical techniques:(i) logistic regression,(ii) quadratic discriminant analysis,(iii) linear discriminant analysis,and (iv) neuronal networks.A Digital Elevation Model (DEM) of 10 m spatial resolution was used to extract the slope angle,aspect,curvature,elevation and relief.These factors,in addition to land cover,lithology and distance to faults,were used as explanatory variables for the susceptibility models.Additionally,a Poisson model was used to estimate landslide temporal frequency,at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements.Then,due to the complexity of evaluating it,vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment (SAVE) model which considered levels of exposure,sensitivity and lack of resilience.Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas.It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments.Consequently,levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.
机译:本文尝试分析墨西哥普埃布拉州帕瓦特兰市的滑坡灾害和脆弱性。为了估算滑坡灾害,感兴趣区域的磁化率,幅度(面积-速度比)和滑坡频率这些数据是根据地貌滑坡清单中的信息生成的;后者是通过使用高分辨率卫星立体声对以及其他来源的信息(Google Earth,航空照片和历史信息)生成的。四种统计技术:(i)Logistic回归,(ii)二次判别分析,(iii)线性判别分析和(iv)神经网络。使用10 m空间分辨率的数字高程模型(DEM)提取倾斜角,纵横比,曲率,高程和起伏。这些因素,除了土地覆盖,岩性和到断层的距离外,还被用作解释变量另外,使用泊松模型估算滑坡的时频,同时利用滑坡面积与运动速度之间的关系获得滑坡的大小。然后,由于评估它的复杂性,利用空间脆弱性评估方法(SAVE)分析人口的脆弱性,该模型考虑了暴露水平,敏感性和缺乏复原力。在表示不同滑坡灾害和脆弱性水平空间分布图的地图上表达了结果值得注意的是,缺乏最佳的方法来评估和量化脆弱性比危害评估更为臭名昭著。因此,与滑坡风险评估相关的不确定性水平仍然是一个亟待解决的挑战。

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