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Spatial databases and GIS as tools for regional landslide susceptibility modeling

机译:空间数据库和GIS作为区域滑坡敏感性模型的工具

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Summary. This study presents a regional landslide susceptibility model for the Federal State of Lower Saxony in the NW part of Central Europe. On the basis of a landslide database and a Geographical Information System (GIS), a modified information value approach is developed, which uses bivariate statistics to predict the spatial distribution and probability of mass movements. The input data of the susceptibility model include a spatial inventory of about 900 landslides and different data sets of geomorphometry, lithology and land use. A regional perspective puts specific requirements on the modeling, which is considered by a reformulation of the weighting function. The model estimates that about 2% of the Lower Saxon territory shows a predisposition to landslides. Most part of this land is concentrated on three key areas in the Lower Saxon Uplands. The spatial pattern of landslide susceptibility is directly correlated with the regional relief configuration and is characterized by specific clusters. In addition to slope gradient between 21° and 49°, Mesozoic sedimentary rock, especially sequences of lime- and claystone, are identified to be the most relevant predisposing factors. Like lithology, land use can be of stabilizing or destabilizing influence, but its significance, however, is less important. A special focus is on the identification of potential infrastructure exposure to landslides. This assessment reveals that in the Lower Saxon Uplands about 1% of the urban area and up to 4% of the road network is located in zones of significant hazard. Although the model is proven to be of good predictive power and high spatial accuracy, the study clarifies that susceptibility modeling often faces methodological shortcomings and a lack of plausibility. Some of these problems are discussed in this paper in detail.
机译:概要。这项研究提出了中欧西北部下萨克森联邦州的区域滑坡敏感性模型。在滑坡数据库和地理信息系统(GIS)的基础上,开发了一种改进的信息价值方法,该方法使用双变量统计信息来预测空间分布和质量运动的概率。敏感性模型的输入数据包括大约900个滑坡的空间清单以及地貌,岩性和土地利用的不同数据集。区域观点对建模提出了特定要求,可以通过重新设计权重函数来考虑。该模型估计,下萨克森州大约2%的地区易发生滑坡。这片土地的大部分集中在下萨克森高地的三个关键地区。滑坡敏感性的空间格局与区域起伏构造直接相关,并具有特定的聚类特征。除了21°至49°的坡度梯度外,中生代沉积岩,尤其是石灰岩和粘土岩序列,被认为是最相关的诱发因素。像岩性一样,土地利用可能具有稳定或破坏稳定的影响,但是其重要性并不那么重要。一个特别的重点是确定潜在的基础设施对滑坡的暴露。该评估表明,在下萨克森州高地地区,大约1%的城市区域和多达4%的道路网络位于严重危险区域。尽管已证明该模型具有良好的预测能力和较高的空间准确性,但研究表明,药敏性建模经常面临方法论上的缺陷和缺乏合理性。本文将详细讨论其中一些问题。

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