scholarly journals Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium

2009 ◽  
Vol 9 (2) ◽  
pp. 507-521 ◽  
Author(s):  
M. Van Den Eeckhaut ◽  
P. Reichenbach ◽  
F. Guzzetti ◽  
M. Rossi ◽  
J. Poesen
Keyword(s):  
Landslide Susceptibility ◽  
Model Performance ◽  
Environmental Information ◽  
Landslide Inventory ◽  
Grid Cells ◽  
Susceptibility Assessment ◽  
Susceptibility Maps ◽  
Validation Set ◽  
Slide Type ◽  
Single Pixel

Abstract. For a 277 km2 study area in the Flemish Ardennes, Belgium, a landslide inventory and two landslide susceptibility zonations were combined to obtain an optimal landslide susceptibility assessment, in five classes. For the experiment, a regional landslide inventory, a 10 m × 10 m digital representation of topography, and lithological and soil hydrological information obtained from 1:50 000 scale maps, were exploited. In the study area, the regional inventory shows 192 landslides of the slide type, including 158 slope failures occurred before 1992 (model calibration set), and 34 failures occurred after 1992 (model validation set). The study area was partitioned in 2.78×106 grid cells and in 1927 topographic units. The latter are hydro-morphological units obtained by subdividing slope units based on terrain gradient. Independent models were prepared for the two terrain subdivisions using discriminant analysis. For grid cells, a single pixel was identified as representative of the landslide depletion area, and geo-environmental information for the pixel was obtained from the thematic maps. The landslide and geo-environmental information was used to model the propensity of the terrain to host landslide source areas. For topographic units, morphologic and hydrologic information and the proportion of lithologic and soil hydrological types in each unit, were used to evaluate landslide susceptibility, including the depletion and depositional areas. Uncertainty associated with the two susceptibility models was evaluated, and the model performance was tested using the independent landslide validation set. An heuristic procedure was adopted to combine the landslide inventory and the susceptibility zonations. The procedure makes optimal use of the available landslide and susceptibility information, minimizing the limitations inherent in the inventory and the susceptibility maps. For the established susceptibility classes, regulations to link terrain domains to appropriate land rules are proposed.

scholarly journals Improving Voting Feature Intervals for Spatial Prediction of Landslides

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Binh Thai Pham ◽  
Tran Van Phong ◽  
Mohammadtaghi Avand ◽  
Nadhir Al-Ansari ◽  
Sushant K. Singh ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Model Performance ◽  
Spatial Prediction ◽  
Susceptibility Assessment ◽  
Improve Performance ◽  
Landslide Susceptibility Assessment ◽  
Ensemble Techniques ◽  
Susceptibility Maps ◽  
Quantitative Validation ◽  
Two Hybrid

In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.

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.

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.

scholarly journals Polygon-based Landslide Inventory for Bandung Basin Using Google Earth

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sukristiyanti Sukristiyanti ◽  
Ketut Wikantika ◽  
Imam A. Sadisun ◽  
Lissa F. Yayusman ◽  
Jevon A. Telaumbanua
Keyword(s):  
Landslide Susceptibility ◽  
Google Earth ◽  
Landslide Inventory ◽  
Susceptibility Assessment ◽  
Visual Interpretation ◽  
Research Papers ◽  
Source Areas ◽  
Landslide Susceptibility Assessment ◽  
Key Factor

A landslide inventory representing landslide locations is used as a key factor in landslide susceptibility assessment. This paper explores Google Earth (GE) for generating a polygon-based landslide inventory in Bandung Basin. How far GE can identify landslides and their boundaries, source areas, and types were discussed here. Visual interpretation of GE images supported by path tool in GE, official landslide reports, previous research papers, and media was performed. The result is a polygon-based landslide inventory consisting of 194 landslide areas and 194 landslide source areas during 1993-2020. The limitations of GE in preparing the landslide inventory are (1) not covering the timing of the landslide occurrences, (2) tricky to identify small landslides (<100 m2) in anthropogenically transformed areas, and (3) not able to distinguish between earth and debris of landslide material.

scholarly journals The importance of input data on landslide susceptibility mapping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Krzysztof Gaidzik ◽  
María Teresa Ramírez-Herrera
Keyword(s):  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Input Data ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Topographic Data ◽  
Susceptibility Maps ◽  
Landslide Mass ◽  
The Impact

AbstractLandslide detection and susceptibility mapping are crucial in risk management and urban planning. Constant advance in digital elevation models accuracy and availability, the prospect of automatic landslide detection, together with variable processing techniques, stress the need to assess the effect of differences in input data on the landslide susceptibility maps accuracy. The main goal of this study is to evaluate the influence of variations in input data on landslide susceptibility mapping using a logistic regression approach. We produced 32 models that differ in (1) type of landslide inventory (manual or automatic), (2) spatial resolution of the topographic input data, (3) number of landslide-causing factors, and (4) sampling technique. We showed that models based on automatic landslide inventory present comparable overall prediction accuracy as those produced using manually detected features. We also demonstrated that finer resolution of topographic data leads to more accurate and precise susceptibility models. The impact of the number of landslide-causing factors used for calculations appears to be important for lower resolution data. On the other hand, even the lower number of causative agents results in highly accurate susceptibility maps for the high-resolution topographic data. Our results also suggest that sampling from landslide masses is generally more befitting than sampling from the landslide mass center. We conclude that most of the produced landslide susceptibility models, even though variable, present reasonable overall prediction accuracy, suggesting that the most congruous input data and techniques need to be chosen depending on the data quality and purpose of the study.

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.

Drawing the landslide susceptibility maps based on long term evolution of extreme rainfall-induced landslide

2020 ◽  
Author(s):  
Chunhung Wu
Keyword(s):  
Landslide Susceptibility ◽  
Extreme Rainfall ◽  
Landslide Inventory ◽  
Landslide Area ◽  
Typhoon Morakot ◽  
Extreme Rainfall Events ◽  
Susceptibility Maps ◽  
Susceptibility Model ◽  
Landslide Ratio ◽  
Landslide Susceptibility Maps

&lt;p&gt;This research is concerned with the prediction accuracy and applicability of statistical landslide susceptibility model to the areas with dense landslide distribution caused by extreme rainfall events and how to draw the annual landslide susceptibility maps after the extreme rainfall events. The landslide induced by 2009 Typhoon Morakot, i.e. an extreme rainfall event, in the Chishan river watershed is dense distributed. We compare the annual landslide inventories in the following 5 years after 2009 Typhoon Morakot and finds the similarity of landslide distribution.&lt;/p&gt;&lt;p&gt;The landslide distributions from 2008 to 2014 are concentrated in the midstream and upstream watersheds. The landslide counts and area in 2009 are 3.4 times and 7.4 times larger than those in 2008 due to 2009 Typhoon Morakot. The landslide counts and area in 2014 are only 69.8% and 53.4 % of those in 2009. The landslide area from 2010 to 2014 shows that the landslide area in the following years after 2009 Typhoon Morakot gradually decreases if without any heavy rainfall event with more accumulated rainfall than that during 2009 Typhoon Morakot.&lt;/p&gt;&lt;p&gt;The landslide ratio in the upstream watershed in 2008 is 1.37%, and that from 2009 to 2014 are over 3.51%. The landslide ratio in the upstream watershed in 2014 is 1.17 times larger than that in 2009. On average, the landslide inventory from 2010 to 2014 in the upstream watershed is composed of 60.1 % old landslide originated from 2009 Typhoon Morakot and 39.9 % new landslide.&lt;/p&gt;&lt;p&gt;The landslide ratio in the midstream watershed reaches peak (9.19%) in 2009 and decreases gradually to 2.56 % in 2014. The landslide ratio in 2014 in the midstream watershed is only 27.9% of that in 2009, and that means around 72.1 % of landslide area in 2009 in the midstream watershed has recovered. On average, the landslide inventory from 2010 to 2014 in the midstream watershed is composed of 76.1 % old landslide originated from 2009 Typhoon Morakot and 23.9 % new landslide.&lt;/p&gt;&lt;p&gt;The research uses the landslide area in 2009 and 2014 in the same subareas to calculate the expanding or contracting ratio of landslide area. The contracting ratio of riverbank and non-riverbank landslide area in the midstream watershed are 0.760 and 0.788, while that in the downstream watershed are 0.732 and 0.789. The expanding ratio of riverbank and non-riverbank landslide area in the upstream watershed are 1.04 and 1.02.&lt;/p&gt;&lt;p&gt;The annual landslide susceptibility in each subarea in the Chishan river watershed in a specific year from 2010 to 2014 is the production of landslide susceptibility in 2009 and the contraction or expanding ratio to the Nth power, and the N number is how many years between 2009 and the specific year. We adopt the above-mentioned equation and the landslide susceptibility model based on the landslide inventory after 2009 Typhoon Morakot to draw the annual landslide susceptibility maps in 2010 to 2014. The mean correct ratio value of landslide susceptibility model in 2009 is 70.9%, and that from 2010 to 2014 are 62.5% to 73.8%.&lt;/p&gt;

scholarly journals The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Kounghoon Nam ◽  
Fawu Wang
Keyword(s):  
Landslide Susceptibility ◽  
Assessment Methods ◽  
Disaster Mitigation ◽  
Susceptibility Assessment ◽  
Ground Acceleration ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Index ◽  
High Possibility ◽  
Northern Japan

Abstract Background Thousands of landslides were triggered by the Hokkaido Eastern Iburi earthquake on 6 September 2018 in Iburi regions of Hokkaido, Northern Japan. Most of the landslides (5627 points) occurred intensively between the epicenter and the station that recorded the highest peak ground acceleration. Hundreds of aftershocks followed the major shocks. Moreover, in Iburi region, there is a high possibility of earthquakes occurring in the future. Effective prediction and susceptibility assessment methods are required for sustainable management and disaster mitigation in the study area. The aim of this study is to evaluate the performance of an autoencoder framework based on deep neural network for prediction and susceptibility assessment of regional landslides triggered by earthquakes. Results By applying 12 sampling sizes and 12 landslide-influencing factors, 12 landslide susceptibility maps were produced using an autoencoder framework. The results of the model were evaluated using qualitative and quantitative assessment methods. The ratios of the sampling sizes on the non-landslide points randomly generated from the combination zone including plain and mountain (PM) and a mountainous only zone (M) affected different prediction abilities of the model’s performance. Conclusions The 12 susceptibility maps, including the landslide susceptibility index, indicated the various spatial distributions of the landslide susceptibility values in both PM and the M. The highly accurate models explicitly distinguished the potential areas of landslide from stable areas without expanding the spatial extent of the potential landslide areas. The autoencoder is proved to be an effective and efficient method for extracting spatial patterns through unsupervised learning for the prediction and susceptibility assessment of landslide areas.

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