scholarly journals A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches

Landslides ◽  
2019 ◽  
Vol 17 (3) ◽  
pp. 627-640 ◽  
Author(s):  
Ting Xiao ◽  
Samuele Segoni ◽  
Lixia Chen ◽  
Kunlong Yin ◽  
Nicola Casagli
Keyword(s):  
Landslide Susceptibility ◽  
Predictive Performance ◽  
Test Site ◽  
Morphological Features ◽  
Landslide Risk ◽  
Simple Method ◽  
Explanatory Variables ◽  
Susceptibility Maps ◽  
Area Under Roc Curve ◽  
Low Altitude

AbstractLandslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units.

scholarly journals Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)

2012 ◽  
Vol 12 (2) ◽  
pp. 327-340 ◽  
Author(s):  
D. Costanzo ◽  
E. Rotigliano ◽  
C. Irigaray ◽  
J. D. Jiménez-Perálvarez ◽  
J. Chacón
Keyword(s):  
Landslide Susceptibility ◽  
Large Scale ◽  
Goodness Of Fit ◽  
Predictive Performance ◽  
Controlling Factors ◽  
Slope Instability ◽  
Susceptibility Maps ◽  
Gis Matrix Method ◽  
Determining Factors ◽  
The One

Abstract. A procedure to select the controlling factors connected to the slope instability has been defined. It allowed us to assess the landslide susceptibility in the Rio Beiro basin (about 10 km2) over the northeastern area of the city of Granada (Spain). Field and remote (Google EarthTM) recognition techniques allowed us to generate a landslide inventory consisting in 127 phenomena. To discriminate between stable and unstable conditions, a diagnostic area had been chosen as the one limited to the crown and the toe of the scarp of the landslide. 15 controlling or determining factors have been defined considering topographic, geologic, geomorphologic and pedologic available data. Univariate tests, using both association coefficients and validation results of single-variable susceptibility models, allowed us to select the best predictors, which were combined for the unique conditions analysis. For each of the five recognised landslide typologies, susceptibility maps for the best models were prepared. In order to verify both the goodness of fit and the prediction skill of the susceptibility models, two different validation procedures were applied and compared. Both procedures are based on a random partition of the landslide archive for producing a test and a training subset. The first method is based on the analysis of the shape of the success and prediction rate curves, which are quantitatively analysed exploiting two morphometric indexes. The second method is based on the analysis of the degree of fit, by considering the relative error between the intersected target landslides by each of the different susceptibility classes in which the study area was partitioned. Both the validation procedures confirmed a very good predictive performance of the susceptibility models and of the actual procedure followed to select the controlling factors.

An advanced method to validate and compare susceptibility maps by investigating local-scale differences and highlighting the role of geomorphological features

2020 ◽  
Author(s):  
Samuele Segoni ◽  
Ting Xiao ◽  
Lixia Chen ◽  
Kunlong Yin ◽  
Nicola Casagli
Keyword(s):  
Test Site ◽  
Systematic Errors ◽  
Quantitative Information ◽  
Local Scale ◽  
The Other ◽  
Hazard Management ◽  
Explanatory Variables ◽  
Fluvial Terraces ◽  
Susceptibility Maps ◽  
Geomorphological Features

<p>In landslide studies, comparing the outcomes obtained by different models is a very robust test for their predictive capability and quantitative indexes are often used to assess which model provides the best predictions. The literature about landslide susceptibility is rich of works where two or more susceptibility models are validated in terms of AUC (area under ROC curve), then the AUC values are compared and the model that provided the highest AUC value is considered the best one.</p><p>The main purpose of this work is to expand this classical approach, which is too simplistic as it neglects any geomorphological consideration, and to propose a new approach that shifts the comparison at the pixel scale, linking the local-scale differences encountered with specific features of the study area. The proposed advanced comparison approach can be summarized with the following steps:</p><ol><li>The susceptibility maps obtained by different models are compared on a pixel-by-pixel basis to define pixels affected by underestimation (UE) and overestimation (OE) of susceptibility values.</li> <li>If present, systematic spatial patterns of UE and OE are identified.</li> <li>The patterns are cross-checked with all the explanatory variables used in the susceptibility assessments.</li> <li>The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility are identified and quantitatively characterized.</li> <li>The quantitative information provided by the previous steps is used to provide a geomorphological interpretation of the differences in the susceptibility values provided by the models, thus adding a more robust element to judge which of them should be used in hazard management, and how.</li> </ol><p>As a case of study, we used four susceptibility maps already defined with random forest (RF), index of entropy (IOE), frequency ratio (FR), and certainty factor (CF) in Wanzhou County (China). A classical validation procedure showed that RF provided the best outcomes, with a 0.801 AUC. After applying the advanced comparison procedure, we obtained deeper insights on the susceptibility models, explaining e.g. why and where RF performed significantly better than the other models and identifying systematic errors that could be associated to distinctive geomorphological features of the test site. Indeed, we discovered that RF is more able to exploit the very complex parameterization of the problem, with 13 parameters, sometimes interrelated each-other, with a total of 80 classes. Moreover, we found that the other models produced systematic errors in correspondence with some lithological units and in fluvial terraces. The area is characterized by 5 orders of relict fluvial terraces, clearly defined only in some small stretches, and the results obtained showed that landsliding has probably been one of the predominant geomorphological process responsible for their depletion.</p>

scholarly journals Retrospective evaluation of landslide susceptibility maps and review of validation practice

2021 ◽  
Vol 80 (15) ◽  
Author(s):  
Paul Fleuchaus ◽  
Philipp Blum ◽  
Martina Wilde ◽  
Birgit Terhorst ◽  
Christoph Butscher
Keyword(s):  
Landslide Susceptibility ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Susceptibility Map ◽  
Retrospective Evaluation ◽  
Widespread Application ◽  
Evaluation Approach ◽  
Susceptibility Maps ◽  
Landslide Event ◽  
Landslide Susceptibility Maps

AbstractDespite the widespread application of landslide susceptibility analyses, there is hardly any information about whether or not the occurrence of recent landslide events was correctly predicted by the relevant susceptibility maps. Hence, the objective of this study is to evaluate four landslide susceptibility maps retrospectively in a landslide-prone area of the Swabian Alb (Germany). The predictive performance of each susceptibility map is evaluated based on a landslide event triggered by heavy rainfalls in the year 2013. The retrospective evaluation revealed significant variations in the predictive accuracy of the analyzed studies. Both completely erroneous as well as very precise predictions were observed. These differences are less attributed to the applied statistical method and more to the quality and comprehensiveness of the used input data. Furthermore, a literature review of 50 peer-reviewed articles showed that most landslide susceptibility analyses achieve very high validation scores. 73% of the analyzed studies achieved an area under curve (AUC) value of at least 80%. These high validation scores, however, do not reflect the high uncertainty in statistical susceptibility analysis. Thus, the quality assessment of landslide susceptibility maps should not only comprise an index-based, quantitative validation, but also an additional qualitative plausibility check considering local geomorphological characteristics and local landslide mechanisms. Finally, the proposed retrospective evaluation approach cannot only help to assess the quality of susceptibility maps and demonstrate the reliability of such statistical methods, but also identify issues that will enable the susceptibility maps to be improved in the future.

scholarly journals Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

2017 ◽  
Vol 17 (7) ◽  
pp. 1091-1109 ◽  
Author(s):  
Sérgio C. Oliveira ◽  
José L. Zêzere ◽  
Sara Lajas ◽  
Raquel Melo
Keyword(s):  
Landslide Susceptibility ◽  
Test Site ◽  
Information Value ◽  
Susceptibility Map ◽  
Predictive Capacity ◽  
Infinite Slope ◽  
Susceptibility Maps ◽  
Basin Scale ◽  
Deterministic Methods ◽  
Physically Based

Abstract. Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km2) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

The necessity to consider the landslide data origin in statistically-based spatial predictive modelling – A landslide intervention index for South Tyrol (Italy)

2020 ◽  
Author(s):  
Stefan Steger ◽  
Volkmar Mair ◽  
Christian Kofler ◽  
Stefan Schneiderbauer ◽  
Marc Zebisch
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Intervention Strategy ◽  
Predictive Performance ◽  
Mixed Effects ◽  
Slope Instability ◽  
Landslide Inventory ◽  
Background Information ◽  
Explanatory Variables ◽  
Landslide Data

<p>Most statistically-based landslide susceptibility maps are supposed to portray the relative likelihood of an area to be affected by future landslides. Literature indicates that vital modelling decisions, such as the selection of explanatory variables, are frequently based on quantitative criteria (e.g. predictive performance). The results obtained by apparently well-performing statistical models are also used to infer the causes of slope instability and to identify landslide “safe” terrain. It seems that comparably few studies pay particular attention to background information associated with the available landslide data. This research hypothesizes that inappropriate modelling decisions and wrong conclusions are likely to follow whenever the origin of the underlying landslide data is ignored. The aims were to (i) analyze the South Tyrolean landslide inventory in the context of its origin in order to (ii) highlight potential pitfalls of performance driven procedures and to (iii) develop a predictive model that takes landslide background information into account. The available landslide data (1928 slide-type movements) of the province of South Tyrol (~7400 km²) consists of positionally accurate points that depict the scarp location of events that induced interventions by e.g. the road service or the geological office. An initial exploratory statistical analysis revealed general relationships between landslide presence/absence data and frequently used explanatory variables. Subsequent modelling was based on a Generalized Additive Mixed Effects Model that allowed accounting for (non-linear) fixed effects and additional “nuisance” variables (random intercepts). The evaluation of the models (diverse variable combinations) focused on modelled relationships, variable importance, spatial and non-spatial predictive performance and the final prediction surfaces. The results highlighted that the best performing models did not reflect the “actual” landslide susceptibility situation. A critical interpretation led to the conclusion that the models simultaneously reflected both, effects likely related to slope instability (e.g. low likelihood of flat and very steep terrain) and effects rather associated with the provincial landslide intervention strategy (e.g. few interventions at high altitudes, increasing number of interventions with decreasing distance to infrastructure). Attempts to separate the nuisance related to “intervention effects” from the actual landslide effects using mixed effects modelling proved to be challenging, also due to omnipresent spatial interrelations among the explanatory variables and the fact that some variables concurrently represent effects related to landslide predisposition and effects associated with the intervention strategy (e.g. altitude). We developed a well-performing predictive landslide intervention index that is in line with the actual data origin and allows identifying areas where future interventions are more or less likely to take place. The efficiency of past interventions (e.g. stabilization of slopes) was demonstrated during recent storm events, because previously stabilized slopes were not affected by new landslides. This also showed that the correct interpretation of the final map requires a simultaneous visualization of both, the spatially predicted index (from low to high) and the available landslide inventory (low likelihood due to past interventions). The results confirm that wrong conclusions can be drawn from excellently performing statistical models whenever qualitative background information is disregarded.</p>

scholarly journals Combination of empirically-based and physically-based methods to assess shallow slides susceptibility at the basin scale

2016 ◽  
Author(s):  
Sérgio C. Oliveira ◽  
José L. Zêzere ◽  
Sara Lajas ◽  
Raquel Melo
Keyword(s):  
Landslide Susceptibility ◽  
Test Site ◽  
Information Value ◽  
Susceptibility Map ◽  
Predictive Capacity ◽  
Infinite Slope ◽  
Susceptibility Maps ◽  
Basin Scale ◽  
Deterministic Methods ◽  
Physically Based

Abstract. Approaches used to assess shallow slides susceptibility at the basin scale are conceptually different depending on the use of empirically-based or physically-based methods. The former are sustained by the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the opposing forces that promote resistance to movement. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodological distinct, the statistic and deterministic methods generate similar shallow slides susceptibility results regarding the model’s predictive capacity and spatial agreement; and (ii) the combination of shallow slides susceptibility maps obtained with empirically-based and physically-based methods, for the same study area, generate a more reliable susceptibility model for shallow slides occurrence. These hypotheses were tested in a small test site (13.9 km2) located north of Lisbon (Portugal), using a empirically-based method (the Information Value method) and a physically-based method (the Infinite Slope method). The landslide susceptibility maps produced with the statistic and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross-tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slides susceptibility map, which combines the independent results obtained with empirically-based and physically-based models. Moreover the combination of the two models allowed the identification of areas where the results of the Information Value and the Infinite Slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

scholarly journals Definition of Environmental Indicators for a Fast Estimation of Landslide Risk at National Scale

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 621
Author(s):  
Samuele Segoni ◽  
Francesco Caleca
Keyword(s):  
Landslide Susceptibility ◽  
Environmental Indicators ◽  
Landslide Risk ◽  
National Scale ◽  
Soil Sealing ◽  
Fast Estimation ◽  
Susceptibility Maps ◽  
Land Consumption ◽  
First Time ◽  
Landslide Susceptibility Maps

The purpose of this paper is to propose a new set of environmental indicators for the fast estimation of landslide risk over very wide areas. Using Italy (301,340 km2) as a test case, landslide susceptibility maps and soil sealing/land consumption maps were combined to derive a spatially distributed indicator (LRI—landslide risk index), then an aggregation was performed using Italian municipalities as basic spatial units. Two indicators were defined, namely ALR (averaged landslide risk) and TLR (total landslide risk). All data were processed using GIS programs. Conceptually, landslide susceptibility maps account for landslide hazard while soil sealing maps account for the spatial distribution of anthropic elements exposed to risk (including buildings, infrastructure, and services). The indexes quantify how much the two issues overlap, producing a relevant risk and can be used to evaluate how each municipality has been prudent in planning sustainable urban growth to cope with landslide risk. The proposed indexes are indicators that are simple to understand, can be adapted to various contexts and at various scales, and could be periodically updated, with very low effort, making use of the products of ongoing governmental monitoring programs of Italian environment. Of course, the indicators represent an oversimplification of the complexity of landslide risk, but this is the first time that a landslide risk indicator has been defined in Italy at the national scale, starting from landslide susceptibility maps (although Italy is one of the European countries most affected by hydro-geological hazards) and, more in general, the first time that land consumption maps are integrated into a landslide risk assessment.

DEVELOPMENT OF LANDSLIDE SUSCEPTIBILITY MAPS IN COLORADO

2016 ◽  
Author(s):  
Kassandra Lindsey ◽  
◽  
Matthew L. Morgan ◽  
Karen A. Berry
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

scholarly journals InSAR Monitoring of Landslide Activity in Dominica

2021 ◽  
Vol 13 (4) ◽  
pp. 815
Author(s):  
Mary-Anne Fobert ◽  
Vern Singhroy ◽  
John G. Spray
Keyword(s):  
Risk Mitigation ◽  
Disaster Mitigation ◽  
Aerial Images ◽  
Landslide Inventory ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Interferometric Synthetic Aperture Radar ◽  
Rainfall Events ◽  
Susceptibility Maps ◽  
Digital Elevation

Dominica is a geologically young, volcanic island in the eastern Caribbean. Due to its rugged terrain, substantial rainfall, and distinct soil characteristics, it is highly vulnerable to landslides. The dominant triggers of these landslides are hurricanes, tropical storms, and heavy prolonged rainfall events. These events frequently lead to loss of life and the need for a growing portion of the island’s annual budget to cover the considerable cost of reconstruction and recovery. For disaster risk mitigation and landslide risk assessment, landslide inventory and susceptibility maps are essential. Landslide inventory maps record existing landslides and include details on their type, location, spatial extent, and time of occurrence. These data are integrated (when possible) with the landslide trigger and pre-failure slope conditions to generate or validate a susceptibility map. The susceptibility map is used to identify the level of potential landslide risk (low, moderate, or high). In Dominica, these maps are produced using optical satellite and aerial images, digital elevation models, and historic landslide inventory data. This study illustrates the benefits of using satellite Interferometric Synthetic Aperture Radar (InSAR) to refine these maps. Our study shows that when using continuous high-resolution InSAR data, active slopes can be identified and monitored. This information can be used to highlight areas most at risk (for use in validating and updating the susceptibility map), and can constrain the time of occurrence of when the landslide was initiated (for use in landslide inventory mapping). Our study shows that InSAR can be used to assist in the investigation of pre-failure slope conditions. For instance, our initial findings suggest there is more land motion prior to failure on clay soils with gentler slopes than on those with steeper slopes. A greater understanding of pre-failure slope conditions will support the generation of a more dependable susceptibility map. Our study also discusses the integration of InSAR deformation-rate maps and time-series analysis with rainfall data in support of the development of rainfall thresholds for different terrains. The information provided by InSAR can enhance inventory and susceptibility mapping, which will better assist with the island’s current disaster mitigation and resiliency efforts.

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