scholarly journals Applicability of Susceptibility Model for Rock and Loess Earthquake Landslides in the Eastern Tibetan Plateau

2021 ◽  
Vol 13 (13) ◽  
pp. 2546
Author(s):  
Xinyi Guo ◽  
Bihong Fu ◽  
Jie Du ◽  
Pilong Shi ◽  
Qingyu Chen ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Disaster Relief ◽  
Coupling Model ◽  
Scientific Data ◽  
Eastern Tibetan Plateau ◽  
Loess Landslide ◽  
Rock Landslide ◽  
Susceptibility Model ◽  
Prediction Capability ◽  
Better Than

It is crucial to explore a suitable landslide susceptibility model with an excellent prediction capability for rapid evaluation and disaster relief in seismic regions with different lithological features. In this study, we selected two typical seismic events, the Jiuzhaigou and Minxian earthquakes, which occurred in the Alpine karst and loess regions, respectively. Eight influencing factors and five models were chosen to calculate the susceptibility of landslide, including the information (I) model, certainty factor (CF) model, logistic regression (LR) model, I + LR coupling model, and CF + LR coupling model. Then, the accuracy and the landslide susceptibility distribution of these models were assessed by the area under curve (AUC) and distribution criteria. Finally, the model with high accuracy and good applicability for the rock landslide or loess landslide regions was optimized. Our results showed that the accuracy of the coupling model is higher than that of the single models. Except for the LR model, the landslide susceptibility distribution for the above-mentioned models is consistent with universal cognition. The coupling models are generally better than their single models. Among them, the I + LR model can obtain the best comprehensive results for assessing the distribution and accuracy of both rock and loess landslide susceptibility, which is helpful for disaster relief and policy-making, and it can also provide useful scientific data for post-seismic reconstruction and restoration.

scholarly journals Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5906
Author(s):  
Xiaoting Zhou ◽  
Weicheng Wu ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
Renxiang Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Landslide Susceptibility ◽  
Urban Areas ◽  
Support Vector ◽  
Susceptibility Map ◽  
Human Society ◽  
Learning Approaches ◽  
Prevention Measures ◽  
Landslide Occurrence ◽  
Better Than

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

2011 ◽  
Vol 37 (4) ◽  
pp. 410-425 ◽  
Author(s):  
C. Melchiorre ◽  
E.A. Castellanos Abella ◽  
C.J. van Westen ◽  
M. Matteucci
Keyword(s):  
Landslide Susceptibility ◽  
Non Linear ◽  
Prediction Capability

scholarly journals Landslide susceptibility assessment in the Peloritani Mts. (Sicily, Italy) and clues for tectonic control of relief processes

2013 ◽  
Vol 13 (4) ◽  
pp. 949-963 ◽  
Author(s):  
G. De Guidi ◽  
S. Scudero
Keyword(s):  
Spatial Data ◽  
Landslide Susceptibility ◽  
Landscape Evolution ◽  
Statistical Technique ◽  
Slope Instability ◽  
Tectonic Activity ◽  
Medium Term ◽  
Tectonic History ◽  
Susceptibility Model ◽  
Morphological Processes

Abstract. Many destructive shallow landslides hit villages in the Peloritani Mountains area (Sicily, Italy) on 1 October 2009 after heavy rainfall. The collection of several types of spatial data, together with a landslide inventory, allows the assessment of the landslide susceptibility by applying a statistical technique. The susceptibility model was validated by performing an analysis in a test area using independent landslide information, the results being able to correctly predict more than 70% of the landslides. Furthermore, the susceptibility analysis allowed the identification of which combinations of classes, within the different factors, have greater relevance in slope instability, and afterwards associating the most unstable combinations (with a short–medium term incidence) with the endogenic processes acting in the area (huge regional uplift, fault activity). Geological and tectonic history are believed to be key to interpreting morphological processes and landscape evolution. Recent tectonic activity was found to be a very important controlling factor in landscape evolution. A geomorphological model of cyclical relief evolution is proposed in which endogenic processes are directly linked to superficial processes. The results are relevant both to risk reduction and the understanding of active geological dynamics.

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.

scholarly journals Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources

2018 ◽  
Vol 10 (2) ◽  
pp. 293 ◽  
Author(s):  
Kyungjin An ◽  
Suyeon Kim ◽  
Taebyeong Chae ◽  
Daeryong Park
Keyword(s):  
Open Source ◽  
Landslide Susceptibility ◽  
Susceptibility Model

scholarly journals Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization

Landslides ◽  
2020 ◽  
Vol 17 (10) ◽  
pp. 2443-2453 ◽  
Author(s):  
Samuele Segoni ◽  
Giulio Pappafico ◽  
Tania Luti ◽  
Filippo Catani
Keyword(s):  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Assessment ◽  
Geological Maps ◽  
Geological Information ◽  
Susceptibility Model ◽  
Validation Procedure ◽  
Geological Map

AbstractThe literature about landslide susceptibility mapping is rich of works focusing on improving or comparing the algorithms used for the modeling, but to our knowledge, a sensitivity analysis on the use of geological information has never been performed, and a standard method to input geological maps into susceptibility assessments has never been established. This point is crucial, especially when working on wide and complex areas, in which a detailed geological map needs to be reclassified according to more general criteria. In a study area in Italy, we tested different configurations of a random forest–based landslide susceptibility model, accounting for geological information with the use of lithologic, chronologic, structural, paleogeographic, and genetic units. Different susceptibility maps were obtained, and a validation procedure based on AUC (area under receiver-operator characteristic curve) and OOBE (out of bag error) allowed us to get to some conclusions that could be of help for in future landslide susceptibility assessments. Different parameters can be derived from a detailed geological map by aggregating the mapped elements into broader units, and the results of the susceptibility assessment are very sensitive to these geology-derived parameters; thus, it is of paramount importance to understand properly the nature and the meaning of the information provided by geology-related maps before using them in susceptibility assessment. Regarding the model configurations making use of only one parameter, the best results were obtained using the genetic approach, while lithology, which is commonly used in the current literature, was ranked only second. However, in our case study, the best prediction was obtained when all the geological parameters were used together. Geological maps provide a very complex and multifaceted information; in wide and complex area, this information cannot be represented by a single parameter: more geology-based parameters can perform better than one, because each of them can account for specific features connected to landslide predisposition.

The Use of LiDAR DTM in Landslide Susceptibility/Hazard Analysis

2020 ◽  
Author(s):  
Chyi-Tyi Lee ◽  
Tsung-Chi Ji
Keyword(s):  
Success Rate ◽  
Landslide Susceptibility ◽  
Hazard Analysis ◽  
Total Curvature ◽  
Measurement Scale ◽  
Slope Aspect ◽  
Related Factor ◽  
Optimal Measurement ◽  
Susceptibility Model ◽  
Landslide Distribution

&lt;p&gt;High-resolution DTM does not always help build a good landslide prediction model. When we are using LiDAR DTM in producing a topographic-related factor for grid-based landslide susceptibility/hazard analysis, the selection of an optimal measurement scale becomes important. Because the resolution of LiDAR DTM may be up to 1 meter, and the average landslide size may be more than 1 thousand square meters, to use a conventional 3x3 kernel for calculation of a factor value is not valid. Actual tests tell us, to use a 15x15 and larger kernel for calculation may yield a more effective factor for interpreting the landslide distribution in a study area.&lt;/p&gt;&lt;p&gt;A test area was selected at the catchment of the Zengwen Reservoir in southwestern Taiwan. The original 1mx1m LiDAR DTM was firstly reduced to a 2mx2m DTM for analysis. Factors of slope gradient, slope aspect, topographic roughness, slope roughness, plan curvature, profile curvature, tangential curvature and total curvature are analyzed by using a series of kernels in different sizes up to 25x25 for comparison. And success rate curve method was used to evaluate the effectiveness of each factor in interpreting landslide distribution. Highest AUC is selected as the most effective one and the kernel size which yield that is the optimal measurement scale of the factor.&lt;/p&gt;&lt;p&gt;A 3x3 kernel has a measurement scale of 2h and is 4 meters (h is grid size of 2 meters), a 25x25 kernel has a measurement scale of 24h and is 48 meters. Factors calculated from an optimal measurement scale will be selected for construction of a landslide susceptibility model. The success rate and prediction rate of this model would be significantly increasing as compared with the model built from conventional 3x3 kernel calculated factors. Finally this optimal susceptibility model was used to construct a landslide hazard model for prediction of landslide distribution under different triggering events.&lt;/p&gt;

Landslide Susceptibility Mapping and Model Performance Assessment

2013 ◽  
Vol 353-356 ◽  
pp. 3487-3493 ◽  
Author(s):  
Chen Chao Xiao ◽  
Yuan Tian ◽  
Kang Ping Si ◽  
Ting Li
Keyword(s):  
Logistic Regression ◽  
Performance Assessment ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Model Performance ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Maps ◽  
Overall Performance ◽  
Better Than

In this paper landslide susceptibility mapping and model performance assessment was conducted using three models, logistic regression, GAM, and SVM, in a study area in Shenzhen, China. Ten factors, slope angle, aspect, elevation, plan and profile curvature of the slope, lithology, NDVI, building density, the distance to the river, and the distance to the fault were selected as influencing factors for the landslide occurrences. All three models were trained and the resulting susceptibility maps were created. The performances of the three models were then assessed by AUC values through a 10-fold cross-validation. It could be concluded that in the study area GAM had the best overall performance among the three models, while SVM was better than logistic regression. Based on the derived DPR values, the optimum thresholds between stable areas and risky areas for all three models were also determined.

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