Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models

Abstract. The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km2) located in the Northern Portugal. In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use) and was compared with a model developed using all seven landslide predisposing factors. Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.

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Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Geosciences ◽

10.3390/geosciences11080333 ◽

2021 ◽

Vol 11 (8) ◽

pp. 333

Author(s):

Massimo Conforti ◽

Fabio Ietto

Keyword(s):

Landslide Susceptibility ◽

Shallow Landslide ◽

Shallow Landslides ◽

Predisposing Factors ◽

Susceptibility Map ◽

Slope Gradient ◽

Landslide Susceptibility Map ◽

Jackknife Test ◽

Susceptibility Model ◽

Very High

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

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Combined rock slope stability and shallow landslide susceptibility assessment of the Jasmund cliff area (Rügen Island, Germany)

Natural Hazards and Earth System Science ◽

10.5194/nhess-9-687-2009 ◽

2009 ◽

Vol 9 (3) ◽

pp. 687-698 ◽

Cited By ~ 27

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.

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A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

10.5194/egusphere-egu21-3415 ◽

2021 ◽

Author(s):

Mariano Di Napoli ◽

Diego Di Martire ◽

Domenico Calcaterra ◽

Marco Firpo ◽

Giacomo Pepe ◽

...

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Shallow Landslide ◽

Shallow Landslides ◽

Predisposing Factors ◽

Mitigation Measures ◽

Economic Losses ◽

Meaningful Improvement ◽

Susceptibility Evaluation ◽

Landslide Runout

Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also &#160;landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the &#8220;reach angle&#8221; method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides&#160; in the future and to identify areas where risk mitigation measures are needed.

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Assessing landslide vulnerability using bivariate statistical analysis and the frequency ratio model. Case study: Transylvanian Plain (Romania)

Zeitschrift für Geomorphologie ◽

10.1127/zfg/2016/0404 ◽

2016 ◽

Vol 60 (4) ◽

pp. 359-371 ◽

Cited By ~ 6

Author(s):

Gheorghe Roşian ◽

Horváth Csaba ◽

Réti Kinga-Olga ◽

Cristian-Nicolae Boţan ◽

Ionela Georgiana Gavrilă

Keyword(s):

Landslide Susceptibility ◽

Frequency Ratio ◽

Slope Angle ◽

Predisposing Factors ◽

Slope Aspect ◽

Susceptibility Map ◽

Ratio Model ◽

Statistical Index ◽

Model Case ◽

Frequency Ratio Model

Landslides are among the most destructive natural hazards in several regions. Here we summarize our findings regarding this phenomenon in the Transylvanian Plain (Romania) using two susceptibility models: the statistical index and the frequency ratio model. Using Esri's ArcGIS Raster Calculator tool we generated susceptibility maps by summarizing the following twelve landslide predisposition factors: lithology, soil type, fault distance, drainage network distance, roads distance, land use (Corrine Land Cover and NDVI), slope angle, aspect, elevation, plan curvature and soil erosion (RUSLE). The landslide susceptibility has been assessed by computing the values for each class of the predisposing factors and thus evaluating the distribution of the landslide zones within each factor, using Esri's Tabulate Area Tool. The extracted predisposing factors maps have then been re-classified on the basis of the computed values in a raster format. Finally, the landslide susceptibility map has been reclassified into five classes using Natural Breaks (Jenks) classification method. The model performance was assessed with Receiver Operating Characteristic (ROC) curve and the R-index. The models with high number of factors had the lowest accuracy (AUC values being <0.8). The best frequency ratio model (AUC = 0.884) contained only three factors (slope, aspect, elevation) while in the case of the statistical index model the best model (AUC = 0.879) contained four factors (slope, aspect, elevation and NDVI). A significant part (33%) of the study area is characterized by a high to very high degree of susceptibility for landslides.

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A simple qualitative approach for mapping regional landslide susceptibility in the Faroe Islands

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-159-2010 ◽

2010 ◽

Vol 10 (2) ◽

pp. 159-170 ◽

Cited By ~ 23

Author(s):

M.-P. J. Dahl ◽

L. E. Mortensen ◽

A. Veihe ◽

N. H. Jensen

Keyword(s):

Landslide Susceptibility ◽

North Atlantic Ocean ◽

Slope Angle ◽

Qualitative Approach ◽

Validation Dataset ◽

Faroe Islands ◽

The North ◽

Susceptibility Model ◽

Map Validation ◽

The Town

Abstract. The Faroe Islands in the North Atlantic Ocean are highly susceptible to landslides. Following recent landslide incidents, Jarðfeingi (Faroese Earth and Energy Directorate) has pointed out, that the risk of human lives or of property being lost or affected by landslides may be increasing. This paper aims at presenting and testing a simple qualitative approach for mapping regional landslide susceptibility in the Faroe Islands, using few key parameters. The susceptibility model holds information about both landslide initiation areas and runout zones. Landslide initiation areas are determined from slope angle thresholds (25°–40°) and soil cover data, while runout zones are delineated using the angle of reach approach taking into account the presence/absence of geological benches in the runout path, which has not been considered in earlier studies. Data input is obtained from a landslide database containing 67 debris flows throughout the Faroe Islands. Angle of reach values differ significantly with the presence/absence of geological benches in the runout path. Two values of angle of reach, 21.5° and 27.6°, are used for calculating runout zones. The landslide susceptibility model is tested in a study area at the town of Klaksvík in the northern part of the Faroe Islands. A map validation comparing predicted susceptibility zones with a validation-dataset of 87 actual landslides in the study area reveal that 69% and 92%, respectively, of actual landslide initiation areas and runout zones are correctly predicted. Moreover 87% of the actual landslides are included in the overall predicted landslide susceptibility areas.

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Susceptibility to Translational Slide-Type Landslides: Applicability of the Main Scarp Upper Edge as a Dependent Variable Representation by Reduced Chi-Square Analysis

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7090336 ◽

2018 ◽

Vol 7 (9) ◽

pp. 336 ◽

Cited By ~ 2

Author(s):

Marco Capitani ◽

Adriano Ribolini ◽

Monica Bini

Keyword(s):

Landslide Susceptibility ◽

Predictive Power ◽

Slope Angle ◽

Related Factor ◽

Chi Square ◽

Related Factors ◽

Main Scarp ◽

Translational Slide ◽

Chi Square Analysis ◽

Variable Representation

The applicability of main scarp upper edge (MSUE) as dependent variable representation was performed in a translational slide susceptibility zonation of the Milia and Roglio basins, Italy. Two landslide inventories were built thanks to detailed geomorphological mapping and aerial photograph analysis. The landslides were used to create the models before 1975, while those after 1975 were employed to validate the predictive power of the model. Possible landslide-related factors were chosen from a geomorphological survey. The inventory landslide maps and the landslide-related factor maps were processed by conditional analysis, producing landslide susceptibility maps with five susceptibility classes. A comparison between the distribution of landslides after 1975 and those derived from models provided the predictive power of each model, which in turn was used to define the best predictive model. Reduced chi-square analysis allowed to define the efficiency of MSUE as dependent variable representation. MSUE can be applied as dependent variable representation to landslide susceptibility zonation with appreciable results. In the Roglio basin, slope angle, distance from streams, and from tectonic lineaments proved to be the main controlling factors of translational slides, whereas in the Milia basin, lithology and slope angle gave more satisfactory results as landslide-predisposing factors.

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Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions

Remote Sensing ◽

10.3390/rs12071194 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1194 ◽

Cited By ~ 1

Author(s):

Deuk-Hwan Lee ◽

Yun-Tae Kim ◽

Seung-Rae Lee

Keyword(s):

Landslide Susceptibility ◽

Activation Function ◽

Shallow Landslide ◽

Predisposing Factors ◽

Landslide Susceptibility Mapping ◽

Activation Functions ◽

Kappa Index ◽

Output Layer ◽

Non Linear ◽

Hidden Layer

Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method.

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GIS-based statistical analysis for assessing shallow-landslide susceptibility along the highway in Calabria (southern Italy)

Rendiconti online della Società Geologica Italiana ◽

10.3301/rol.2015.184 ◽

2016 ◽

Vol 39 ◽

pp. 155-158 ◽

Cited By ~ 1

Author(s):

Massimo Conforti ◽

Valeria Rago ◽

Francesco Muto ◽

Pasquale Versace

Keyword(s):

Statistical Analysis ◽

Landslide Susceptibility ◽

Southern Italy ◽

Shallow Landslide

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

Remote Sensing ◽

10.3390/rs13132546 ◽

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.

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