Validation of spatial prediction models for landslide hazard mapping
Validation of spatial prediction models for landslide hazard mapping
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DOI：
10.1023/b:nhaz.0000007172.62651.2b
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发表时间：
2003-11-01
期刊：
影响因子：
3.7
通讯作者：
Fabbri, AG
中科院分区：
文献类型：
--
作者：
Chung, CJF;Fabbri, AG
这篇文章讨论了提供预测结果的重要性的措施时，从空间数据库中产生的预测滑坡灾害地图的问题。空间数据库通常包含关于岩性单位、土地覆盖单位、地形高程和衍生属性（坡度、坡向等）的地图信息。以及明确的群众运动在空间和时间上的分布。在预测建模中，我们将多层数据库转换为函数值的集合，以获得土地破坏倾向的指数。假设数据库中的信息足以代表质量运动在空间和时间上起源的典型条件，那么问题就是要确认某些模型的结果相对于其他模型的有效性，或者似乎使用更重要数据的特定实验的有效性。衡量预测重要性的一个核心点是它允许解释结果。如果没有验证，就不可能进行解释，也不能提供对方法或输入信息的支持。特别是通过验证，可以在固定的时间间隔内或在开放的时间内或在研究区域的有限空间内评估预测的附加值。验证对滑坡灾害制图的数据收集和野外实践具有指导意义。
This contribution discusses the problem of providing measures of significance of prediction results when the predictions were generated from spatial databases for landslide hazard mapping. The spatial databases usually contain map information on lithologic units, land- cover units, topographic elevation and derived attributes ( slope, aspect, etc.) and the distribution in space and in time of clearly identified mass movements. In prediction modelling we transform the multi- layered database into an aggregation of functional values to obtain an index of propensity of the land to failure. Assuming then that the information in the database is sufficiently representative of the typical conditions in which the mass movements originated in space and in time, the problem then, is to confirm the validity of the results of some models over other ones, or of particular experiments that seem to use more significant data. A core point of measuring the significance of a prediction is that it allows interpreting the results. Without a validation no interpretation is possible, no support of the method or of the input information can be provided. In particular with validation, the added value can be assessed of a prediction either in a fixed time interval, or in an open- ended time or within the confined space of a study area. Validation must be of guidance in data collection and field practice for landslide hazard mapping.