Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units
Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units
复制标题
使用随机森林和多元自适应回归样条模型结合流域图单元对滑坡敏感性图进行比较
DOI：
10.1007/s12303-018-0038-8
复制
发表时间：
2019-04-01
影响因子：
1.2
通讯作者：
Zhang, Jinchi
中科院分区：
文献类型：
--
作者：
Chu, Lei;Wang, Liang-Jie;Zhang, Jinchi
滑坡易发性制图是减轻地质灾害危害的重要手段。地图单元和数学模型的选择对最小二乘方法的效率有很大影响。为了获得地图单元和数学模型的最佳组合，分析了4种尺度的集水地图单元（CMU），并应用随机森林（RF）和多元自适应回归样条（MAR样条）模型在热罗市，日本。滑坡识别率和相对工作特征曲线（ROC）下的面积被用来评估模型的性能。结果表明，RF模型比MAR样条模型具有更高的预测精度，特别是当CMU为0.09km2时，预测精度更高。相对较高比例的滑坡属于高和非常高的滑坡易感性类（73%）和滑坡的最低比例属于非常低的滑坡易感性类（0.82%）。预测面积（P-A）图表明，RF模型的预测率高于MAR样条模型。本研究的结果还表明，如果使用适当的CMU大小，可以提高模型的精度。因此，应使用额外的滑坡调节因子和其他模型，进一步探讨使用RF模型结合适当的CMU大小的潜在好处。
Landslide susceptibility mapping (LSM) is a critical tool for mitigating the damages caused by geologic disasters. The selection of map units and mathematical models greatly affects the efficiency of LSM. To obtain the most appropriate combination of map units and mathematical models, four scales of catchment map units (CMUs) were analyzed and random forest (RF) and multivariate adaptive regression spline (MARSpline) models were applied in Gero City, Japan. The percentage of correctly identified landslides and the areas under the relative operating characteristic (ROC) curve were used to evaluate the model performances. The results indicate that the RF model had higher prediction accuracy than the MARSpline model, especially when the size of the CMU was 0.09 km(2). A relatively high percentage of landslides fell into the high and very high landslide susceptibility classes (73%) and the lowest percentage of landslides fell into the very low landslide susceptibility classes (0.82%). The prediction-area (P-A) plots indicated that the prediction rates were higher for the RF model than the MARSpline model. The results of this study also suggest that the model accuracy can be increased if the appropriate CMU size is used. Therefore, the potential benefits of using the RF model in combination with the appropriate CMU size should be further explored using additional landslide-conditioning factors and other models.