Modelling Shallow Landslide Risk Using GIS and a Distributed Hydro-geotechnical Model

2015 ◽  
pp. 221-240
Author(s):  
Pingping Luo ◽  
Apip ◽  
Bin He ◽  
Kaoru Takara ◽  
Weili Duan ◽  
...  
Keyword(s):  
Shallow Landslide ◽  
Landslide Risk ◽  
Geotechnical Model

A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale

Landslides ◽  
2010 ◽  
Vol 7 (3) ◽  
pp. 237-258 ◽  
Author(s):  
Apip ◽  
Kaoru Takara ◽  
Yosuke Yamashiki ◽  
Kyoji Sassa ◽  
Agung Bagiawan Ibrahim ◽  
...  
Keyword(s):  
Shallow Landslide ◽  
Prediction System ◽  
Catchment Scale ◽  
Landslide Prediction ◽  
Geotechnical Model ◽  
Rainfall Estimates

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

Risk Analysis ◽  
2019 ◽  
Vol 39 (11) ◽  
pp. 2576-2595 ◽  
Author(s):  
Jean Baptiste Nsengiyumva ◽  
Geping Luo ◽  
Egide Hakorimana ◽  
Richard Mind'je ◽  
Aboubakar Gasirabo ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Shallow Landslide ◽  
Landslide Risk ◽  
Risk Modeling

Cost-based analysis of mitigation measures for shallow-landslide risk reduction strategies

2016 ◽  
Vol 213 ◽  
pp. 142-157 ◽  
Author(s):  
Jorge Pedro Galve ◽  
Andrea Cevasco ◽  
Pierluigi Brandolini ◽  
Daniela Piacentini ◽  
José Miguel Azañón ◽  
...  
Keyword(s):  
Risk Reduction ◽  
Shallow Landslide ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Reduction Strategies ◽  
Risk Reduction Strategies ◽  
Landslide Risk Reduction

Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modelling

Landslides ◽  
2014 ◽  
Vol 12 (1) ◽  
pp. 101-114 ◽  
Author(s):  
Jorge Pedro Galve ◽  
Andrea Cevasco ◽  
Pierluigi Brandolini ◽  
Mauro Soldati
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Risk Mitigation ◽  
Shallow Landslide ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Probabilistic Modelling

scholarly journals Shallow landslide prediction and analysis with risk assessment using a spatial model in the coastal region in the state of São Paulo, Brazil

2013 ◽  
Vol 1 (5) ◽  
pp. 5199-5236
Author(s):  
P. I. M. Camarinha ◽  
V. Canavesi ◽  
R. C. S. Alvalá
Keyword(s):  
Risk Assessment ◽  
Spatial Data ◽  
Urban Areas ◽  
Coastal Region ◽  
Spatial Prediction ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Serra Do Mar

Abstract. In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil – Santos, Cubatão, Caraguatatuba and Ubatuba – in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

scholarly journals Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 488 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Giuseppe Bausilio ◽  
Domenico Calcaterra ◽  
Pierluigi Confuorto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Geographical Information ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Learning Techniques

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

The Potential Role of Tree Diversity in Reducing Shallow Landslide Risk

2017 ◽  
Vol 59 (5) ◽  
pp. 807-815 ◽  
Author(s):  
Yuta Kobayashi ◽  
Akira S. Mori
Keyword(s):  
Potential Role ◽  
Shallow Landslide ◽  
Tree Diversity ◽  
Landslide Risk
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