Landslide triggering by rain infiltration

Landsliding in response to precipitation involves physical processes that care for disparate timescales. Relationships between these timescales guide development of a mathematical model that uses reduced kinds of I. A. Richards equation to judge effects of precipitation infiltration on landslide prevalence, timing, depth, and acceleration in numerous things. The longest pertinent timescale is A/D0, wherever D0 is that the most hydraulic diffusivity of the soil and A is that the geographic region that doubtless affects groundwater pressures at a prospective landslide slip surface location with region coordinates x, y and depth H. Times larger than A/D0 area unit necessary for institution of steady background water pressures that develop at (x, y, H) in response to precipitation averaged over periods that normally vary from days to several decades. [1]


The most common varieties of mass movements that type landslide dams are rock and soil slumps and slides; mud, debris, and earth flows: and rock and junk avalanches. the foremost common initiation mechanisms for dam-forming landslides are excessive downfall and snow soften, and earthquakes. Most landslide dams are outstanding passing. in an exceedingly sample of sixty three documented cases, twenty two p.c of the landslide dams unsuccessful in but one day when formation, and [*fr1] unsuccessful at intervals ten days. Overtopping was out and away the foremost frequent reason for landslide-dam failure. Backwater flooding behind landslide dams will inundate communities and valuable agricultural land. Floods from the failure of landslide dams are smaller than floods from created dams internment bodies of water with constant mechanical energy, however larger than floods from failure of ice dams. Secondary effects of landslide-dam failures embrace further landslides as reservoir levels drop quickly, aggradation of valleys upstream and downstream of the dams, and avulsive channel changes downstream. [2]

Landslide Hazard and Risk Assessment

Landslides play a crucial role within the evolution of landforms and represent a heavy hazard in several areas of the planet. In places, fatalities and economic injury caused by landslides are larger than those caused by alternative natural hazards, as well as earthquakes, volcanic eruptions and floods. thanks to the extraordinary breadth of the spectrum of landslide phenomena, no single technique exists to spot and map landslides, to determine landslide hazards, and to guage the associated risk. This work contributes to cut back this disadvantage by providing the scientific principle, a standard language, and a collection of valid tools for the preparation and also the best use of landslide maps, component prediction models, and landslide forecasts. [3]

Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features

Landslide disasters cause vast casualties and economic losses once a year, the way to accurately forecast the landslides has continually been a vital issue in geo-environment analysis. during this paper, a hybrid machine learning approach RSLMT is foremost planned by coupling Random topological space (RS) and provision Model Tree (LMT) for manufacturing a landslide status map (LSM). With this methodology, the uncertainty introduced by input options is taken into account, the matter of overfitting is resolved by reducing dimensions to extend the prediction rate of landslide incidence. Moreover, the uncertainty of prediction are going to be deeply mentioned with the rank chance score (RPS) series, that is a vital analysis of uncertainty however seldom utilized in LSM. [4]

Landslide Hazard Zonation (LHZ) Mapping Using RS and GIS Techniques: A Case Study of Kumbur River Basin of Kodaikanal Taluk, Dindigul District, Tamilnadu, India

Landslide is one in all the disasters that cause large-scale injury to properties and life. It oftentimes happens in craggy regions like Himalaya, Western and jap Ghats. In Madras, most of the landslides square measure usually seen in Blue Mountains, Kodaikanal and Yercaud, often within the different areas. Kodaikanal hills face 2 major issues viz. urbanization and environmental degradation. during this study, the landslide hazard zonation maps square measure ready supported the precipitating factors of slope instability, particularly thick soil accumulation, lithology, geologic structure drain density, slope morphometry, relative relief, land use and land cowl and hydrogeological conditions in aspect wise by mistreatment BIS code: IS 14496 (Part-2) – 1998. As per BIS classification technique, Kumbur geographical {area|geographic area|geographical region|geographic region} area, the distribution pattern of Landslide Hazard Zonation (LHZ) indicates that within the total eighty two sides, three sides return underneath terribly high hazard class, seventeen sides return underneath high hazard class, forty sides square measure gift in moderate hazards and remaining twenty five sides return underneath direct low hazard. [5]


[1] Iverson, R.M., 2000. Landslide triggering by rain infiltration. Water resources research, 36(7), (Web Link)

[2] Schuster, R.L. and Costa, J.E., 1986. PERSPECTIVE ON LANDSLIDE DAMS. In Landslide Dams: Processes, Risk, and Mitigation. Proceedings of a Session in Conjunction with the ASCE Convention. (Web Link)

[3] Varnes, D.J., 1984. Landslide hazard zonation: a review of principles and practice (No. 3). (Web Link)

[4] Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features
Xiangang Luo, Feikai Lin, Yihong Chen, Shuang Zhu, Zhanya Xu, Zhibin Huo, Mengliang Yu & Jing Peng
Scientific Reports volume 9, Article number: 15369 (2019) (Web Link)

[5] Mahesh, R., Baskaran, R. and Anbalagan, R. (2018) “Landslide Hazard Zonation (LHZ) Mapping Using RS and GIS Techniques: A Case Study of Kumbur River Basin of Kodaikanal Taluk, Dindigul District, Tamilnadu, India”, Journal of Geography, Environment and Earth Science International, 16(4), (Web Link)

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