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Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia

机译:马来西亚槟城岛的概率性滑坡灾害和风险图

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This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/ geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.
机译:本文利用地理信息系统(GIS)和遥感数据处理了马来西亚槟城岛的滑坡灾害和风险分析。研究区域的滑坡位置通过航拍照片的解释和实地调查确定。使用GIS和图像处理工具收集并处理了地形/地质数据和卫星图像。滑坡灾害分析考虑了十个滑坡诱发参数。这些参数是地形坡度,纵横比,曲率和距排水的距离,所有这些参数均来自地形数据库;来自地质数据库的地质和距地貌的距离;来自Landsat卫星图像的土地利用;土壤数据库中的土壤;降水量,来自降雨数据库;以及来自SPOT卫星图像的植被指数值。使用概率频率比模型,使用滑坡发生因子分析滑坡敏感性。使用滑坡位置数据验证了分析结果,并与概率模型进行了比较。观察到的准确度为80.03%。通过频率比模型,通过GIS环境下的地图叠加分析,对滑坡进行了定性分析。危害图的准确率为86.41%。此外,通过研究滑坡灾害图和处于危险中的可损坏物体来进行风险分析。该信息可用于估计人口,财产和现有基础设施(如交通网络)的风险。

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