Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java

2017 ◽  
Vol 76 (4) ◽  
Author(s):  
Guruh Samodra ◽  
Guangqi Chen ◽  
Junun Sartohadi ◽  
Kiyonobu Kasama
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Inventory ◽  
Data Driven

scholarly journals An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

2020 ◽  
Vol 9 (10) ◽  
pp. 561
Author(s):  
Omid Ghorbanzadeh ◽  
Khalil Didehban ◽  
Hamid Rasouli ◽  
Khalil Valizadeh Kamran ◽  
Bakhtiar Feizizadeh ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Data Driven ◽  
Susceptibility Maps ◽  
Normalised Difference Vegetation Index ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Sentinel 2

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

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

A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping

CATENA ◽  
2018 ◽  
Vol 166 ◽  
pp. 317-327 ◽  
Author(s):  
A-Xing Zhu ◽  
Yamin Miao ◽  
Rongxun Wang ◽  
Tongxin Zhu ◽  
Yongcui Deng ◽  
...  
Keyword(s):  
Comparative Study ◽  
Landslide Susceptibility ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Data Driven ◽  
Landslide Susceptibility Mapping ◽  
Knowledge Based

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.

Mapping landslide susceptibility using data-driven methods

2017 ◽  
Vol 589 ◽  
pp. 250-267 ◽  
Author(s):  
J.L. Zêzere ◽  
S. Pereira ◽  
R. Melo ◽  
S.C. Oliveira ◽  
R.A.C. Garcia
Keyword(s):  
Landslide Susceptibility ◽  
Data Driven ◽  
Using Data
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