A landslide ternary diagram for geometric form and topographic site in Taiwan
A landslide ternary diagram for geometric form and topographic site in Taiwan
复制标题
DOI：
10.1007/s10346-020-01507-2
复制
发表时间：
2020-08
期刊：
影响因子：
6.7
通讯作者：
S. Tfwala;Chia-Ling Huang;C. Tsou;Su-chin Chen
中科院分区：
文献类型：
--
作者：
S. Tfwala;Chia-Ling Huang;C. Tsou;Su-chin Chen
台湾因降雨事件引发的自然灾害激增，台风、暴雨无不引发重大山体滑坡。在这项研究中，利用 2006 年至 2014 年间发生的主要降雨引发的滑坡数据（总共 605 起），将滑坡分为以下类型：浅层滑坡（SL，495）、大型滑坡（LL，34）和泥石流（DF，76）。 LL 被定义为面积、深度和体积分别大于 10 ha、2 m 和 2 × 105m3 的滑坡。然后通过三元图分析它们的几何形状、地理分布、规模和体积特征。 SL 的长度（L）和体积（V）之间存在显着的线性趋势，随着长度的增加，趋势逐渐缓和并与 LL 收敛。 LL的体积随深度(H)呈显着增加趋势，而SL和DF的深度较小且分布均匀。 SL和LL的滑坡长宽比中值十分接近，且形态也较为相似；然而，SL往往发生在坡脚附近，而大型LL由于其体积大，起源于山脊附近并延伸到最近的溪流。 SL 的 W(β1) 和 L(β2) 的幂律缩放分量由于 (SL) 小而相似，并且高度集中在展开的三元图的中心。通过逻辑回归，我们进一步验证了滑坡分类的指数；三元图中使用了β1、β2和β3（H的幂律缩放分量）。总体而言，β1 被认为是分类 DF、SL 和 LL 的最佳模型，其正确率为 0.955，最低的赤池信息准则 (AIC) 为 136.115，贝叶斯信息准则 (BIC) 为 153.736.β3，虽然深度指数的 AIC 较差，但对 LL 的分类正确率为 100%。
The number of natural disasters induced by rainfall events in Taiwan has soared, with typhoons and torrential rains invariably inducing major landslides. In this study, data on major rainfall-generated landslides (605 in total) which occurred between 2006 and 2014 were used to classify landslides as types: shallow landslides (SL, 495), large landslides (LL, 34), and debris flows (DF, 76). LL were defined as landslides having an area, depth, and volume greater than 10 ha, 2 m and 2 × 105m3, respectively. These were then analysed for their geometric form, geographic distribution, and scale and volume characteristics through a ternary diagram. A significant linear trend was found between the length (L) and volume (V) of SL, with the trend gradually moderating and converging with LL as length increased. The volume of LL displayed a significant increasing trend with depth (H), while SL and DF had less depth and average distribution. The median landslide length/width (L/W) ratios of SL and LL were quite close, and they had relatively similar morphologies; however, SL tended to occur near the slope toe, while large LL, due to their large volume, originated near the mountain ridges and extended to the nearest streams. The power law scaling components ofW(β1) andL(β2) of SL were similar because of their (SL) small size, and they were highly concentrated at the centre of the developed ternary diagram. Through logistic regression, we further validated the exponents in classifying the landslides;β1,β2, andβ3(power law scaling component ofH) are used in the ternary diagram. Overall,β1was found to be the best model for classifying DF, SL, and LL having a correct rate of 0.955 and a lowest Akaike information criterion (AIC), 136.115, and Bayesian information criterion (BIC), 153.736.β3, the depth index, though had a poor AIC, was 100% correct in classifying LL.