Department of Surveying Engineering, Islamic Azad University Saghez Branch, Saghez, Iran.
Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran.
Sci Total Environ. 2020 Nov 25;745:141008. doi: 10.1016/j.scitotenv.2020.141008. Epub 2020 Jul 20.
Snow avalanches can destroy lives and infrastructure and are very important phenomena in some regions of the world. This study maps snow avalanche susceptibility in Sirvan Watershed, Iran, using a new approach. Two statistical models - belief function (Bel) and probability density (PD) - are combined with two learning models - multi-layer perceptron (MLP) and logistic regression (LR) - to predict avalanche susceptibility using remote sensing data in a geographic information system (GIS). A snow avalanche inventory map was generated from Google Earth imagery, regional documentation, and field surveys. Of 101 avalanche locations, 71 (70%) were used to train the models and 30 (30%) were used to validate the resulting models. Fourteen snow avalanche conditioning factors were used as independent variables in the predictive modeling process. First, the weight of Bel and PD techniques were applied to each class of factors. Then, they were combined with two MLP and LR learning models for snow avalanche susceptibility mapping (SASM). The results were validated using positive predictive values, negative predictive values, sensitivity, specificity, accuracy, root-mean-square error, and area-under-the-curve (AUC) values. Thus, the AUCs for the PD-LR, Bel-LR, Bel-MLP, and PD-MLP hybrid models are 0.941, 0.936, 0.931 and 0.924, respectively. Based on the validation results, the PD-LR hybrid model achieved the best accuracy among the models. This hybrid modeling approach can provide accurate and reliable evaluations of snow avalanche-prone areas for management and decision making.
雪崩会摧毁生命和基础设施，在世界上一些地区是非常重要的现象。本研究使用一种新方法对伊朗锡尔万流域的雪崩敏感性进行了制图。两种统计模型——置信函数（Bel）和概率密度（PD）——与两种学习模型——多层感知机（MLP）和逻辑回归（LR）相结合，利用地理信息系统（GIS）中的遥感数据来预测雪崩敏感性。雪崩隐患图是根据谷歌地球图像、区域文献和实地调查生成的。在 101 个雪崩地点中，有 71 个（70%）用于训练模型，有 30 个（30%）用于验证模型。在预测建模过程中，使用了 14 个雪崩条件因素作为独立变量。首先，对 Bel 和 PD 技术的权重进行了应用，然后将其与两种 MLP 和 LR 学习模型结合起来进行雪崩敏感性制图（SASM）。使用阳性预测值、阴性预测值、敏感性、特异性、准确性、均方根误差和曲线下面积（AUC）值对结果进行了验证。因此，PD-LR、Bel-LR、Bel-MLP 和 PD-MLP 混合模型的 AUC 分别为 0.941、0.936、0.931 和 0.924。根据验证结果，PD-LR 混合模型在模型中具有最高的准确性。这种混合建模方法可以为管理和决策提供对雪崩易发区的准确可靠评估。
