Early warning system for shallow landslides using rainfall threshold and slope stability analysis

Naidu, Shruti; Sajinkumar, K.S.; Oommen, Thomas; Anuja, V.J.; Samuel, Rinu; Muraleedharan, C.

doi:10.1016/j.gsf.2017.10.008

Cited by 126 publications

(62 citation statements)

References 32 publications

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“…La implementación de modelos físicos para definir umbrales de lluvia suele ser basada en una correlación con el factor de seguridad (FS) asociado a la ocurrencia de deslizamientos [12,13]. Esta aplicación suele desarrollarse para sitios específicos, de modo que el análisis de estabilidad de taludes se puede acoplar con metodologías de umbrales de lluvia como un modelo integrado, lo cual representa un gran aporte para el desarrollo de sistemas de alerta temprana de movimientos en masa [14].…”

Section: Introductionunclassified

Rainfall thresholds for shallow landslides based on physical models: application in a sub-basin of the Valle de Aburrá (Colombia)•

Marin

Aristizábal

2019

DYNA

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An analysis of the rainfall conditions and the influence of the infiltration processes on the slope stability are important in tropical mountain terrains due to the wide occurrence of rainfall-induced landslides. Distributed physical-based models can be coupled with techniques that relate the factor of safety with rainfall thresholds associated with the occurrence of this phenomenon. This study presents a methodology that determines the thresholds of rainfall intensity and duration, for the prediction of shallow landslides, using a distributed physical model called TRIGRS. In the analysis, it is used a percentage of the critical area of failure defined for a study area. The conditions of mean intensity and duration of rain that induce this failing area to be reached are adjusted to a potential equation, within certain ranges of duration and intensity. The model is used in a sub-basin of the Valle de Aburrá (Colombia) and the results are compared with rainfall thresholds defined with another distributed physical model. The obtained results reaffirm that the conditions of rainfall intensity and duration that trigger landslides in a region exhibit scaling properties determined by relationships of potential equations, like those obtained by other researchers in different places of the world.

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Section: Introductionunclassified

Rainfall thresholds for shallow landslides based on physical models: application in a sub-basin of the Valle de Aburrá (Colombia)•

Marin

Aristizábal

2019

DYNA

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“…Terrain slope: The fact that terrain slope is one of the most significant factors in landslide causatives. A review by Reichenbach, Rossi [2] and Naidu, Sajinkumar [3] has confirmed the critical role of slope in instability conditions of the landslide. In this study, terrain slope in the study area is investigated from the STRM DEM (30x30m) using the Arc GIS 10.4.1.…”

Section: Figure 3 Flowchart Of the Methodology Adopted For This Studymentioning

confidence: 93%

“…Paola Reichenbach et al (2018) classed 23 landslide impact factors into five groups of causatives which are: geological, hydrological, land cover, morphological and other factors. Nevertheless, the proportion of contribution of the causative factors to landslide occurrence is different depending on spatial, temporal distributions of the event [1,2,3,4,5]. Recent studies which focus on Asian countries such as China, Taiwan, Korea and Japan indicate that duration and magnitude of rainfall are primary causatives of landslides in mountainous areas [3,5].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the proportion of contribution of the causative factors to landslide occurrence is different depending on spatial, temporal distributions of the event [1,2,3,4,5]. Recent studies which focus on Asian countries such as China, Taiwan, Korea and Japan indicate that duration and magnitude of rainfall are primary causatives of landslides in mountainous areas [3,5]. Once, rainfall runoff entering topsoil which surfaces layer with a loose structure in a multi-day period combined with terrain slope would lead to destabilization and landslide in this layer.…”

Section: Introductionmentioning

confidence: 99%

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Studies on the causes of landslides for mountainous regions in central region of Vietnam

Chí¹

2019

jst

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Landslide is a complex geo-hazard which impacts on sustainable social-economic development in the mountain area. This phenomenon is a result of a combination of critical natural and artificial conditions of many impact factors such as topographic attribute, infrastructure constructions, geology, land cover, and rainfall. Estimating the contribution weights of these factors plays a significant role in disaster management activities. This study focuses on three provinces which are Hue, Quang Nam and Quang Ngai which are frequently and severely impacted by landslide in the central region of Vietnam. Historical events are investigated by statistical analysis, field survey with supports from GIS to figure out these significant factors to landslide occurring in the study area. The result has illustrated landslide increases, according to the development of human activities and long duration critical rainfall.

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“…Some limitations include the fact that feature classes do not always correspond to data classes, therefore time needs to be spent after the classification is complete to analyze the results, and the fact that feature properties can change over time [32,33]. Nonetheless, prior studies have shown that these classification techniques provide an opportunity to analyze earth surfaces applied to landslide detection [34][35][36][37][38]. The coupling of airborne LiDAR-derived DEMs with unsupervised classification methods offer an opportunity to develop semi-automated algorithms that can help support the initial process of landslide mapping.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Classification for Landslide Detection from Airborne Laser Scanning

et al. 2019

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Landslides are natural disasters that cause extensive environmental, infrastructure and socioeconomic damage worldwide. Since they are difficult to identify, it is imperative to evaluate innovative approaches to detect early-warning signs and assess their susceptibility, hazard and risk. The increasing availability of airborne laser-scanning data provides an opportunity for modern landslide mapping techniques to analyze topographic signature patterns of landslide, landslide-prone and landslide scarred areas over large swaths of terrain. In this study, a methodology based on several feature extractors and unsupervised classification, specifically k-means clustering and the Gaussian mixture model (GMM) were tested at the Carlyon Beach Peninsula in the state of Washington to map slide and non-slide terrain. When compared with the detailed, independently compiled landslide inventory map, the unsupervised methods correctly classify up to 87% of the terrain in the study area. These results suggest that (1) landslide scars associated with past deep-seated landslides may be identified using digital elevation models (DEMs) with unsupervised classification models; (2) feature extractors allow for individual analysis of specific topographic signatures; (3) unsupervised classification can be performed on each topographic signature using multiple number of clusters; (4) comparison of documented landslide prone regions to algorithm mapped regions show that algorithmic classification can accurately identify areas where deep-seated landslides have occurred. The conclusions of this study can be summarized by stating that unsupervised classification mapping methods and airborne light detection and ranging (LiDAR)-derived DEMs can offer important surface information that can be used as effective tools for digital terrain analysis to support landslide detection.

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Early warning system for shallow landslides using rainfall threshold and slope stability analysis

Cited by 126 publications

References 32 publications

Rainfall thresholds for shallow landslides based on physical models: application in a sub-basin of the Valle de Aburrá (Colombia)•

Rainfall thresholds for shallow landslides based on physical models: application in a sub-basin of the Valle de Aburrá (Colombia)•

Studies on the causes of landslides for mountainous regions in central region of Vietnam

Unsupervised Classification for Landslide Detection from Airborne Laser Scanning

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