(01)00087-73ĭille A, Kervyn F, Mugaruka Bibentyo T, Delvaux D, Ganza GB, Ilombe Mawe G, Kalikone Buzera C, Safari Nakito E, Moeyersons J, Monsieurs E, Nzolang C, Smets B, Kervyn M, Dewitte O (2019) Causes and triggers of deep-seated hillslope instability in the tropics †Insights from a 60-year record of Ikoma landslide (DR Congo). rep., National Academy of Science, Washingtonĭai F, Lee C (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. URL 002887Ĭruden DM, Varnes D (1996) Landslide types and processes. Nat Hazards Earth Syst Sci 2(1–2):57–72Ĭhigira M, Yagi H (2006) Geological and geomorphological characteristics of landslides triggered by the 2004 Mid Niigta prefecture earthquake in Japan. Ĭardinali M, Reichenbach P, Guzzetti F, Ardizzone F, Antonini G, Galli M, Cacciano M, Castellani M, Salvati P (2002) A geomorphological approach to estimate landslide hazard and risk in urban and rural areas in Umbria, central Italy. Geosciences (Switzerland) 8(12), DOI īorgomeo E, Hebditch KV, Whittaker AC, Lonergan L (2014) Characterising the spatial distribution, frequency and geomorphic controls on landslide occurrence, Molise, Italy. URL 300108īordoni M, Valentino R, Meisina C, Bittelli M, Chersich S (2018) A simplified approach to assess the soil saturation degree and stability of a representative slope affected by shallow landslides in oltrepò pavese (Italy). īhardwaj A, Wasson RJ, Ziegler AD, Chow WT, Sundriyal YP (2019) Characteristics of rain-induced landslides in the Indian Himalaya: a case study of the Mandakini Catchment during the 2013 flood. β 3, the depth index, though had a poor AIC, was 100% correct in classifying LL.Īkaike H (1974) A new look at the statistical model identification. Overall, β 1 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. Through logistic regression, we further validated the exponents in classifying the landslides β 1, β 2, and β 3 (power law scaling component of H) are used in the ternary diagram. The power law scaling components of W ( β 1) and L ( β 2) of SL were similar because of their (SL) small size, and they were highly concentrated at the centre of the developed ternary diagram. 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 volume of LL displayed a significant increasing trend with depth ( H), while SL and DF had less depth and average distribution. 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. These were then analysed for their geometric form, geographic distribution, and scale and volume characteristics through a ternary diagram. LL were defined as landslides having an area, depth, and volume greater than 10 ha, 2 m and 2 × 10 5 m 3, respectively. 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). The number of natural disasters induced by rainfall events in Taiwan has soared, with typhoons and torrential rains invariably inducing major landslides.