Landslide inventory mapping and landslide susceptibility modeling assessment on the SW flank of Pico de Orizaba volcano, Puebla-Veracruz, Mexico

2013 ◽  
Vol 57 (3) ◽  
pp. 371-385 ◽  
Author(s):  
Gabriel Legorreta Paulín ◽  
Marcus Bursik ◽  
María Teresa Ramírez-Herrera ◽  
Trevor Contreras ◽  
Michael Polenz ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Inventory ◽  
Susceptibility Modeling ◽  
Pico De Orizaba Volcano

scholarly journals INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE

2016 ◽  
Vol XLI-B8 ◽  
pp. 89-93
Author(s):  
J.-S. Lai ◽  
F. Tsai ◽  
S.-H. Chiang
Keyword(s):  
Random Forests ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Digital Terrain Model ◽  
Landslide Inventory ◽  
Terrain Model ◽  
Susceptibility Modeling ◽  
Causative Factors

This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.

scholarly journals Statistical and expert-based landslide susceptibility modeling on a national scale applied to North Macedonia

2019 ◽  
Vol 11 (1) ◽  
pp. 750-764
Author(s):  
Ivica Milevski ◽  
Slavoljub Dragićević ◽  
Matija Zorn
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Analytical Hierarchy Process ◽  
Landslide Susceptibility ◽  
Cross Validation ◽  
Landslide Inventory ◽  
National Scale ◽  
Landslide Occurrence ◽  
Susceptibility Modeling ◽  
Hierarchy Process

Abstract This article presents a Geographic Information System (GIS) assessment of Landslide Susceptibility Zonation (LSZ) in North Macedonia. Because of the weak landslide inventory, statistical method (frequency ratio) is combined with Analytical Hierarchy Process (AHP). In this study, lithology, slope, plan curvature, precipitations, land cover, distance from streams, and distance from roads were selected as precondition factors for landslide occurrence. There are two advantages of the approach used. The first is the possibility of comparing of the results and cross-validation between the statistical and expert based methods with an indication of the advantages and drawbacks of each of them. The second is the possibility of better weighting of precondition factors for landslide occurrence, which can be useful in cases of weak landslide inventory. The final result shows that in the case of weak landslide inventory, LSZmap created with the combination of both models provide better overall results than each model separately.

scholarly journals Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling

2019 ◽  
Vol 8 (9) ◽  
pp. 397 ◽  
Author(s):  
Lai ◽  
Chiang ◽  
Tsai
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Inventory ◽  
Sampling Strategies ◽  
Remotely Sensed Data ◽  
Sensitive Analysis ◽  
Susceptibility Modeling ◽  
Hybrid Sample ◽  
Integrated Cost ◽  
Event Based ◽  
Mixing Problem

This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with diverse topographic conditions. The second issue is the mixing problem of landslide inventory that the detection of landslide areas from remotely-sensed data generally includes source and run-out features unless the run-out portion can be removed manually with auxiliary data. To this end, different statistical sampling strategies and the run-out influence on random forests (RF)-based landslide susceptibility modeling are explored for Typhoon Morakot in 2009 in southern Taiwan. To address the construction of models with an extremely high false alarm error or missing error, this study integrated cost-sensitive analysis with RF to adjust the decision boundary to achieve improvements. Experimental results indicate that, compared with a logistic regression model, RF with the hybrid sample strategy generally performs better, achieving over 80% and 0.7 for the overall accuracy and kappa coefficient, respectively, and higher accuracies can be obtained when the run-out is treated as an independent class or combined with a non-landslide class. Cost-sensitive analysis significantly improved the prediction accuracy from 5% to 10%. Therefore, run-out should be separated from the landslide source and labeled as an individual class when preparing a landslide inventory.

scholarly journals INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE

2016 ◽  
Vol XLI-B8 ◽  
pp. 89-93
Author(s):  
J.-S. Lai ◽  
F. Tsai ◽  
S.-H. Chiang
Keyword(s):  
Random Forests ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Digital Terrain Model ◽  
Landslide Inventory ◽  
Terrain Model ◽  
Susceptibility Modeling ◽  
Causative Factors

This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.

Implications of landslide inventory in susceptibility modeling along a Himalayan highway corridor, India

2021 ◽  
pp. 1-24
Author(s):  
Vijendra Kumar Pandey ◽  
Arun Kumar Tripathi ◽  
Kaushal Kumar Sharma
Keyword(s):  
Landslide Inventory ◽  
Susceptibility Modeling

The Landslide Susceptibility Assessment using Bi-variate Statistical Information Value Model of Chenab River Valley, Jammu and Kashmir (India)

2021 ◽  
Vol 14 (11) ◽  
pp. 44-56
Author(s):  
Abhijit S. Patil ◽  
Bidyut K. Bhadra ◽  
Sachin S. Panhalkar ◽  
Sudhir K. Powar
Keyword(s):  
Landslide Susceptibility ◽  
Statistical Information ◽  
River Valley ◽  
Information Value ◽  
Landslide Inventory ◽  
Susceptibility Map ◽  
Acceptable Result ◽  
Information Value Model ◽  
Value Model ◽  
Chenab River

Almost every year, the Himalayan region suffers from a landslide disaster that is directly associated with the prosperity and development of the area. The study of landslide disasters helps planners, decision-makers and local communities for the development of anthropogenic structures in order to enhance the safety of society. Therefore, the prime aim of this research is to produce the landslide susceptibility map for the Chenab river valley using the bi-variate statistical information value model to detect and demarcate the areas of potential landslide incidence. The object-based image analysis method identified about 84 potential sites of landslides as landslide inventory. The statistical information value model is derived from the landslide inventory and multiple causative factors. The outcome showed that 23% area of the Chenab river valley falls into the class of a very high landslide susceptibility zone. The ROC curve method is used to validate the model which denoted the acceptable result for the landslide susceptibility zonation with 0.826 AUC value for the Chenab river valley.

Communality from equal weight for GIS-based landslide susceptibility mapping

2020 ◽  
Author(s):  
Sandip Som ◽  
Saibal Ghosh ◽  
Soumitra Dasgupta ◽  
Thrideep Kumar ◽  
J. N. Hindayar ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Roc Curves ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Equal Weight ◽  
Landslide Susceptibility Index

Abstract Modeling landslide susceptibility is one of the important aspects of land use planning and risk management. Several modeling methods are available based either on highly specialized knowledge on causative attributes or on good landslide inventory data to use as training and testing attribute on model development. Understandably, these two criteria are rarely available for local land regulators. This paper presents a new model methodology, which requires minimum knowledge of causative attributes and does not depend on landslide inventory. As landslide causes due to the combined effect of causative attributes, this model utilizes communality (common variance) of the attributes, extracted by exploratory factor analysis and used for calculation of landslide susceptibility index. The model can understand the inter-relationship of different geo-environmental attributes responsible for landslide along with identification and prioritization of attributes on model performance to delineate non-performing attributes. Finally, the model performance is compared with the well established AHP method (knowledge driven) and FRM method (data driven) by cut-off independent ROC curves along with cost-effectiveness. The model shows it’s performance almost at par with the established models, involving minimum modeling expertise. The findings and results of the present work will be helpful for the town planners and engineers on a regional scale for generalized planning and assessment.

scholarly journals Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

2020 ◽  
Vol 10 (18) ◽  
pp. 6335 ◽  
Author(s):  
Kamila Pawluszek-Filipiak ◽  
Natalia Oreńczak ◽  
Marta Pasternak
Keyword(s):  
Landslide Susceptibility ◽  
Research Question ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
High Susceptibility ◽  
Geological Structures ◽  
Area Index ◽  
Landslide Occurrence ◽  
Very High

To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

scholarly journals Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 106 ◽  
Author(s):  
Qingfeng He ◽  
Zhihao Xu ◽  
Shaojun Li ◽  
Renwei Li ◽  
Shuai Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Entropy Method ◽  
Decision Tree Classifier ◽  
Rotation Forest ◽  
Conditioning Factors ◽  
Susceptibility Modeling ◽  
Landslide Hazards ◽  
Tree Classifier ◽  
Hybrid Intelligence

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

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