Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.
Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.
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DOI：
10.1371/journal.pone.0133262
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Zhu Z
中科院分区：
文献类型：
--
作者：
Dou J;Tien Bui D;Yunus AP;Jia K;Song X;Revhaug I;Xia H;Zhu Z
本文评估了确定性因子模型（CF）的潜力，最适合的滑坡易感性制图在日本新泻县佐渡岛的成因提取因子。为了测试CF的适用性，国家地球科学与灾害预防研究所（NIED）提供的滑坡清单地图被分成两个子集：（i）70%的滑坡清单将用于构建CF模型;（ii）30%的滑坡将用于验证目的。通过对ALOS卫星图像、航空照片、地形图和地质图的处理，建立了包含15个滑坡成因因子的空间数据库。然后应用CF模型从15个因素中选择最佳子集。使用所有15个因素和最佳子集的因素，滑坡易感性地图使用统计指数（SI）和逻辑回归（LR）模型。利用验证数据中的滑坡位置对磁化率图进行了验证和比较。使用受试者工作特征（ROC）估计两种易感性图的预测性能。结果表明，LR模型的ROC曲线下面积（AUC）（AUC = 0.817）略高于SI模型（AUC = 0.801）。此外，应注意，使用最佳子集的SI和LR模型优于使用十五个原始因子的模型。因此，我们得出结论，优化因子模型，使用CF是更准确地预测滑坡易感性，并获得一个更均匀的分类图。我们的研究结果承认，在山区遭受数据稀缺，它是可能的选择关键因素相关的滑坡发生的CF模型的基础上，在GIS平台。因此，以有效的方式为未来的风险缓解规划制定了设想方案。
This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.