Landslide recognition: Identification, movement, and causes

Wilkerson, Forrest D.

doi:10.1016/s0169-555x(97)00047-0

Cited by 17 publications

(9 citation statements)

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“…Landslides that show no recent evidence of erosion or movement were classified as inactive. They are less distinctive, especially in the field (Dikau et al., 1996; Mather et al., 2003). In theory, they can re‐activate randomly with return periods ranging from thousands of years to a few decades (Flageollet, 1996).…”

Section: Resultsmentioning

confidence: 99%

“…To compile a more complete landslide inventory, we focused on the landform characteristics of landslides across the cirque . We evaluated five indexed morphologic pieces of evidence: (a) main and secondary scarps, (b) depressions, (c) lobes and areas of material accumulation, (d) open cracks and fissures, and (e) vegetation (Dikau et al., 1996; Mather et al., 2003; McCalpin, 1984; Nichol et al., 2006) and their relationship to landslide activity (e.g., Parise & Guzzi, 1992; Turner & Schuster, 1996; Wieczorek, 1984). The fresher and more visible the morphologic indices are, the more recent and more active the processes are (Flageollet, 1996).…”

Section: Methodsmentioning

confidence: 99%

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Landslide Processes Involved in Volcano Dismantling From Past to Present: The Remarkable Open‐Air Laboratory of the Cirque de Salazie (Reunion Island)

Rault¹,

Thiéry

Chaput³

et al. 2022

JGR Earth Surface

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Volcanic edifices often exhibit an eventful geological history characterized by constructive and destructive phases (Carracedo et al., 1999;Gayer et al., 2021;Thouret, 1999;Zernack & Procter, 2021). Volcanic terrains are characterized by a combination of specific environmental factors that make them prone to landslides, such as high relief, steep slopes, weathered and fractured materials, alternating layers of lavas and volcaniclastic deposits with different mechanical characteristics, and preferential shear surfaces (Roverato et al., 2015;Scott et al., 2001;Tibaldi et al., 2008). They are also exposed to processes known to trigger failures, such as volcanic activity with hydrothermal circulation, fluid injection, earthquakes (Di Traglia et al., 2018;Roverato et al., 2021;Schaefer et al., 2019) and high precipitation rates, especially in tropical environments.Volcanic edifices are prone to the full spectrum of landslide sizes ranging from giant debris or rock avalanches (RA) of several km 3 to small rockfalls, including:1. Large-scale volcanic landslides (e.g., slumps and debris avalanches [DA]) with various magnitudes and kinematics may occur several times during the evolution of a volcano. These landslides can reach volumes of several km 3 , which can correspond to the destabilization of an entire volcanic flank (Blahůt et al., 2019;

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Landslide Processes Involved in Volcano Dismantling From Past to Present: The Remarkable Open‐Air Laboratory of the Cirque de Salazie (Reunion Island)

Rault¹,

Thiéry

Chaput³

et al. 2022

JGR Earth Surface

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show abstract

“…On the contrary, overturning and translational landslides extend at most over a few tens to a few hundreds of meters. These mass movements can also be classified in term of their activity-type according to the typology of Varnes in [30], and are from the largest to the smallest: The abandoned dormant type defined as inactive but is no longer affected by the initial activating causes by they have reached a certain stability. The forms are fossile and vegetation is beginning to cover the movement.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Coastal Retreat and Slope Movements on Rocky Coastal Cliffs: A Distributed Natural Hazard in the Safi Region, Morocco

Imam

Chaibi²,

Ougougdal³

et al. 2023

Iecg 2022

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The coastal area of Safi between Cap Beddouza and Jorf Lihoudi is dominated by different forms of marine cliffs forming the coastline, with rock formations ranging from Jurassic to Quaternary. The main coastal geological risk is the instabilities of these cliffs due to falls and collapses of blocks as well as landslides. The aim of this paper is to give detailed information on the rate of cliff summit recession for the last 66 years, obtained by applying the DSAS model, in parallel with the identification of unstable elements, including their different parameters such as size, abundance, activity in time and their effect on the recession of the cliff top, which are fundamental information for the evolution models of sea cliffs. These phenomena constitute a significant economical, social, and environmental risk.

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“…Among them, MR is the most important. MR is a parameter proposed by Melton (1965) [60] to reflect the dynamic characteristics of the catchment and the debris flow susceptibility; it is widely used in the evaluation and classification of debris flows [43,50,51,61]. This is followed by DD, HI, and AS, which are related to the geomorphic conditions.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway

et al. 2020

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The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.

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Landslide recognition: Identification, movement, and causes

Cited by 17 publications

References 0 publications

Landslide Processes Involved in Volcano Dismantling From Past to Present: The Remarkable Open‐Air Laboratory of the Cirque de Salazie (Reunion Island)

Landslide Processes Involved in Volcano Dismantling From Past to Present: The Remarkable Open‐Air Laboratory of the Cirque de Salazie (Reunion Island)

Analysis of Coastal Retreat and Slope Movements on Rocky Coastal Cliffs: A Distributed Natural Hazard in the Safi Region, Morocco

Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway

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