GIS-based landslide susceptibility model considering effective contributing area for drainage time

2017 ◽  
Vol 33 (8) ◽  
pp. 810-829 ◽  
Author(s):  
Ananta Man Singh Pradhan ◽  
Yun-Tae Kim
Keyword(s):  
Landslide Susceptibility ◽  
Drainage Time ◽  
Susceptibility Model

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 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

<p>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.</p><p>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.</p><p>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.</p>

Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach

Landslides ◽  
2017 ◽  
Vol 15 (4) ◽  
pp. 753-772 ◽  
Author(s):  
Haoyuan Hong ◽  
Biswajeet Pradhan ◽  
Maher Ibrahim Sameen ◽  
Bahareh Kalantar ◽  
Axing Zhu ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Model

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

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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%.</p>

scholarly journals Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping

2020 ◽  
Vol 12 (9) ◽  
pp. 1486
Author(s):  
Tania Luti ◽  
Samuele Segoni ◽  
Filippo Catani ◽  
Michele Munafò ◽  
Nicola Casagli
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Categorical Variable ◽  
Operating Characteristics ◽  
Mapping Unit ◽  
Explanatory Variables ◽  
The Road ◽  
Soil Sealing ◽  
Susceptibility Model ◽  
Remote Sensing Techniques

Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC ((area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter “soil sealing aggregation” significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures.

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