Landslide hazard assessment: recent trends and techniques

Pardeshi, Sudhakar D.; Autade, Sumant Eknath; Pardeshi, Suchitra S

doi:10.1186/2193-1801-2-523

Cited by 189 publications

(96 citation statements)

References 91 publications

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“…As a result of this, instead of using estimated probabilities directly in the form of return periods of observed landslides or expected values for risks resulting from landslides, models use probability estimates as relative indices (e.g., Pack et al, 1998) that can be used for hazard zonation (Pardeshi et al, 2013). A lack of temporal dimension limits the incorporation of model results into risk assessments and the decision-making processes in high-risk regions.…”

Section: Geomorphology and Modeling Backgroundmentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km 2 . The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at midelevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

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Section: Geomorphology and Modeling Backgroundmentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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“…Due to the potential loss of properties and lives, which may result from such phenomena, landslide assessment and monitoring is one of the main responsibilities in each community in order to give a perspective for stabilization processes (Avşar et al, 2014;Mizal-Azzmi et al, 2011;Mulia and Prasetyorini, 2013). In this regard, different geodetic techniques can provide important insights to this problem (Delacourt et al, 2007;Gili et al, 2000;Pardeshi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Landslide Monitoring Using Insar Time-Series and GPS Observations, Case Study: Shabkola Landslide in Northern Iran

Mirzaee¹,

Motagh

Akbari³

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Shabkola is a village located in Mazandaran province of northern Iran that suffers from the mass movement happening in the upstream. Deforestation and changes to land use are the main reasons for the soil instability in this region, which together with steep slope, relatively high precipitation rate and natural erosion has led to such a condition. The area of mass movement is approximately 90 hectares which is a big threat for people living in the region. In this study, we have utilized two different geodetic techniques including InSAR time-series analysis and GPS measurements to assess slope stability in Shabkola. The SAR dataset includes 19 ALOS/PALSAR images spanning from July 2007 to February 2011 while GPS observations are collected in 5 campaigns from September 2011 to May 2014. Displacement as much as approximately 11.7 m in slope direction was detected by GPS observations for the 2011-2014 time period. Most of the slope geometry is in north-south direction, for which the sensitivity of InSAR for displacement detection is low. However, ALOS PALSAR data analysis revealed a previously unknown landslide, covered by dense vegetation in the northern part of main Shabkola landslide, showing line-of-sight velocity of approximately 2cm/year in the time period 2007-2011.

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“…The best landslide model for an area depends not only on the quality of the data used (Jebur et al, 2014) This article discusses the process of landslide susceptibility mapping used in the current literatures. Some of the early good reviews on landslide hazard assessments can be found on: Kanungo et al, (2009), andPardeshi et al, (2013). Also, a case study of developing landslide susceptibility map in the 2015 earthquake hit region of Sindhupalchowk, Nepal using FR (Frequency Ratio) has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping for 2015 Earthquake Region of Sindhupalchowk, Nepal using Frequency Ratio

Yang¹,

Acharya²,

Lee³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Globally, landslides triggered by natural or human activities have resulted in enormous damage to both property and life. Recent climatic changes and anthropogenic activities have increased the number of occurrence of these disasters. Despite many researches, there is no standard method that can produce reliable prediction. This article discusses the process of landslide susceptibility mapping using various methods in current literatures and applies the FR (Frequency Ratio) method to develop a susceptibility map for the 2015 earthquake region of Sindhupalchowk, Nepal. The complete mapping process describes importance of selection of area, and controlling factors, widespread techniques of modelling and accuracy assessment tools. The FR derived for various controlling factors available were calculated using pre-and post-earthquake landslide events in the study area and the ratio was used to develop susceptibility map. Understanding the process could help in better future application process and producing better accuracy results. And the resulting map is valuable for the local general and authorities for prevention and decision making tasks for landslide disasters. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Landslide hazard assessment: recent trends and techniques

Cited by 189 publications

References 91 publications

A hydroclimatological approach to predicting regional landslide probability using Landlab

A hydroclimatological approach to predicting regional landslide probability using Landlab

Landslide Monitoring Using Insar Time-Series and GPS Observations, Case Study: Shabkola Landslide in Northern Iran

Landslide Susceptibility Mapping for 2015 Earthquake Region of Sindhupalchowk, Nepal using Frequency Ratio

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