Shallow landslide susceptibility analysis in relation to land use scenarios

2016 ◽  
pp. 1605-1612 ◽  
Author(s):  
M Persichillo ◽  
M Bordoni ◽  
C Meisina ◽  
C Bartelletti ◽  
R Giannecchini ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Susceptibility Analysis ◽  
Land Use Scenarios

Shallow landslide susceptibility analysis in relation to land use scenarios

2018 ◽  
pp. 1605-1612
Author(s):  
M.G. Persichillo ◽  
M. Bordoni ◽  
C. Meisina ◽  
C. Bartelletti ◽  
R. Giannecchini ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Susceptibility Analysis ◽  
Land Use Scenarios

scholarly journals Investigation on Farmland Abandonment of Terraced Slopes Using Multitemporal Data Sources Comparison and Its Implication on Hydro-Geomorphological Processes

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1552 ◽  
Author(s):  
Pepe ◽  
Mandarino ◽  
Raso ◽  
Scarpellini ◽  
Brandolini ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Temporal Analysis ◽  
Shallow Landslide ◽  
Farmland Abandonment ◽  
Geomorphological Processes ◽  
Lower Magnitude ◽  
The Impact ◽  
Terraced Slopes

This paper presents a quantitative multi-temporal analysis performed in a GIS environment and based on different spatial information sources. The research is aimed at investigating the land use transformations that occurred in a small coastal terraced basin of Eastern Liguria from the early 1950s to 2011. The degree of abandonment of cultivated terraced slopes together with its influence on the distribution, abundance, and magnitude of rainfall-induced shallow landslides were accurately analysed. The analysis showed that a large portion of terraced area (77.4%) has been abandoned over approximately sixty years. This land use transformation has played a crucial role in influencing the hydro-geomorphological processes triggered by a very intense rainstorm that occurred in 2011. The outcomes of the analysis revealed that terraces abandoned for a short time showed the highest landslide susceptibility and that slope failures affecting cultivated zones were characterized by a lower magnitude than those which occurred on abandoned terraced slopes. Furthermore, this study highlights the usefulness of cadastral data in understanding the impact of rainfall-induced landslides due to both a high spatial and thematic accuracy. The obtained results represent a solid basis for the investigation of erosion and the shallow landslide susceptibility of terraced slopes by means of a simulation of land use change scenarios.

scholarly journals Combined rock slope stability and shallow landslide susceptibility assessment of the Jasmund cliff area (Rügen Island, Germany)

2009 ◽  
Vol 9 (3) ◽  
pp. 687-698 ◽  
Author(s):  
A. Günther ◽  
C. Thiel
Keyword(s):  
Slope Stability ◽  
Landslide Susceptibility ◽  
Slope Failure ◽  
Rock Slope ◽  
Shallow Landslide ◽  
Material Strength ◽  
Spatial Integration ◽  
Inventory Data ◽  
Susceptibility Analysis ◽  
Susceptibility Model

Abstract. In this contribution we evaluated both the structurally-controlled failure susceptibility of the fractured Cretaceous chalk rocks and the topographically-controlled shallow landslide susceptibility of the overlying glacial sediments for the Jasmund cliff area on Rügen Island, Germany. We employed a combined methodology involving spatially distributed kinematical rock slope failure testing with tectonic fabric data, and both physically- and inventory-based shallow landslide susceptibility analysis. The rock slope failure susceptibility model identifies areas of recent cliff collapses, confirming its value in predicting the locations of future failures. The model reveals that toppling is the most important failure type in the Cretaceous chalk rocks of the area. The shallow landslide susceptibility analysis involves a physically-based slope stability evaluation which utilizes material strength and hydraulic conductivity data, and a bivariate landslide susceptibility analysis exploiting landslide inventory data and thematic information on ground conditioning factors. Both models show reasonable success rates when evaluated with the available inventory data, and an attempt was made to combine the individual models to prepare a map displaying both terrain instability and landslide susceptibility. This combination highlights unstable cliff portions lacking discrete landslide areas as well as cliff sections highly affected by past landslide events. Through a spatial integration of the rock slope failure susceptibility model with the combined shallow landslide assessment we produced a comprehensive landslide susceptibility map for the Jasmund cliff area.

LANDSLIDE SUSCEPTIBILITY ANALYSIS BASED ON A SEMIQUANTITATIVE METHOD IN THE SIERRA-COSTA REGION, MICHOACÁN, MEXICO

2021 ◽  
pp. 1-24
Author(s):  
Karla Aurora De La Peña Guillen ◽  
Manuel E. Mendoza ◽  
José Luis Macías ◽  
Berenice Solis-Castillo
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Analysis ◽  
Semiquantitative Method

scholarly journals Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Mei Yang ◽  
Jingjue Chen ◽  
Tianqiang Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Hybrid Model ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Support Vector ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment

2009 ◽  
Vol 99 (3) ◽  
pp. 661-674 ◽  
Author(s):  
María José Domínguez-Cuesta ◽  
Montserrat Jiménez-Sánchez ◽  
Ana Colubi ◽  
Gil González-Rodríguez
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
New Approach
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