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A landslide ternary diagram for geometric form and topographic site in Taiwan
Landslides ( IF 6.7 ) Pub Date : 2020-08-13 , DOI: 10.1007/s10346-020-01507-2 Samkele Tfwala , Chia-Ling Huang , Ching-Ying Tsou , Su-Chin Chen
Landslides ( IF 6.7 ) Pub Date : 2020-08-13 , DOI: 10.1007/s10346-020-01507-2 Samkele Tfwala , Chia-Ling Huang , Ching-Ying Tsou , Su-Chin Chen
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 × 105 m3, 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 of W (β1) and L (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, and β3 (power law scaling component of H) are used in the ternary diagram. Overall, β1 was 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.
更新日期：2020-08-13
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