Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan.
Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan.
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DOI：
10.1016/j.scitotenv.2018.10.431
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
M. Juliev;M. Mergili;I. Mondal;B. Nurtaev;A. Pulatov;J. Hübl
中科院分区：
文献类型：
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作者：
M. Juliev;M. Mergili;I. Mondal;B. Nurtaev;A. Pulatov;J. Hübl
乌兹别克斯坦Bostanlik地区的特点是山区地形容易发生山体滑坡。本研究的目的是为该地区的部分地区绘制一份统计得出的滑坡易感性地图，这是乌兹别克斯坦的第一个此类地图，以便为风险管理提供信息。采用统计指数（SI）、频率比（FR）和确定性因子（CF）进行比较。十个预测层用于分析，包括地质、土壤、土地利用和土地覆盖、坡度、坡向、海拔、到地貌的距离、到断层的距离、到道路的距离和到溪流的距离。基于GeoEye-1和谷歌地球图像绘制了170个滑坡多边形。其中随机抽取119个（70%）用于方法训练，保留51个（30%）用于结果评价。3幅滑坡易感性图分为极低、低、中、高、极高5个等级。对所得结果的评价建立在成功率曲线和预测率曲线下的面积（AUC）上。SI、FR和CF方法的训练准确率分别为82.1%、74.3%和74%，预测准确率分别为80%、70%和71%。滑坡和预测层之间的空间关系证实了之前在其他地区进行的研究结果，而模型的性能略高于早期的一些研究，这可能是基于多边形的滑坡清单的好处。
The Bostanlik district, Uzbekistan, is characterized by mountainous terrain susceptible to landslides. The present study aims at creating a statistically derived landslide susceptibility map – the first of its type for Uzbekistan - for part of the area in order to inform risk management. Statistical index (SI), frequency ratio (FR) and certainty factor (CF) are employed and compared for this purpose. Ten predictor layers are used for the analysis, including geology, soil, land use and land cover, slope, aspect, elevation, distance to lineaments, distance to faults, distance to roads, and distance to streams. 170 landslide polygons are mapped based on GeoEye-1 and Google Earth imagery. 119 (70%) out of them are randomly selected and used for the training of the methods, whereas 51 (30%) are retained for the evaluation of the results. The three landslide susceptibility maps are split into five classes, i.e. very low, low, moderate, high, and very high. The evaluation of the results obtained builds on the area under the success rate and prediction rate curves (AUC). The training accuracies are 82.1%, 74.3% and 74%, while the prediction accuracies are 80%, 70% and 71%, for the SI, FR and CF methods, respectively. The spatial relationships between the landslides and the predictor layers confirmed the results of previous studies conducted in other areas, whereas model performance was slightly higher than in some earlier studies – possibly a benefit of the polygon-based landslide inventory.