scholarly journals Evaluation of the landslide susceptibility map obtained by a GIS matrix method: a case of Al Hoceima city (northern Morocco)

2021 ◽  
Vol 119 ◽  
pp. 04002
Author(s):  
Taoufik Byou
Keyword(s):  
Landslide Susceptibility ◽  
Matrix Method ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Susceptibility Map ◽  
Susceptibility Maps ◽  
Gis Matrix Method ◽  
The Matrix ◽  
The One ◽  
Al Hoceima

This work presents a method for preparing landslide susceptibility maps based on an analysis between past movements and predisposing factors. The proposed methodology for determining susceptibility is an adaptation of the matrix method in the territory of the city of Al Hoceima (Northern Morocco). The data needed to evaluate vulnerability has based on the landslide inventory and the available cartographic documents. These data were prepared and integrated into a GIS. This phase of the data preparation has followed by assessment and susceptibility mapping. The obtained map was validated and compared with an independent landslide dataset than the one used to develop the model. The results of the ROC (receiver operating characteristic) curve demonstrate the good predictive ability (AUC = 0.94). We can deduce that the map correlates well with the existing terrain conditions. The successfully validated prediction will enable decision-makers to offer handy information for infrastructure construction and urban planning in the future or other areas with similar situations

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.

scholarly journals Validation of landslide susceptibility maps (Case study: Urban area of the municipality of Banja Luka - B&H)

2017 ◽  
Vol 97 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Novica Lovric ◽  
Radislav Tosic
Keyword(s):  
Urban Area ◽  
Landslide Susceptibility ◽  
Matrix Method ◽  
Statistical Index ◽  
Index Method ◽  
Good Opportunity ◽  
Susceptibility Maps ◽  
Gis Matrix Method ◽  
Banja Luka ◽  
Landslide Susceptibility Maps

Landslides represent a serious geo-hazard in many areas of the world. They are one of the most damaging and most significant geo-hazards in Bosnia and Herzegovina. In the previous research, three GIS-based methodologies (Index based method, Statistical index method, and Landslide susceptibility analysis), have been used to assess the landslide susceptibility in urban area of the Municipality of Banja Luka. Validation technique is performed by comparing existing landslide data from 2012 with obtained landslide susceptibility maps. In this research, the landslide susceptibility maps were supplemented by landslide susceptibility map which is prepared using a GIS Matrix method. The area and percentage distribution of the susceptibility classes in the study area were determined as a result of the four different techniques. The Statistical index method has provided the most satisfying results. As a consequence of heavy rains during the period from May to August 2014, 126 landslides occurred in the study area, and they offer a good opportunity to validate obtained landslide susceptibility maps of the study area with landslides that occurred in different periods of time. Validation was performed by using a ?degree of fit? method. The values of the degree of fit in high and very high category in all used methods are over 80%, except for GIS matrix method, where the percentage is 60%. The validation of obtained landslide susceptibility maps suggests that the applications of all the used techniques provide a good basis for creation landslide susceptibility maps, but the best results are given by using a Statistical index method.

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 Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2292 ◽  
Author(s):  
Vali Vakhshoori ◽  
Hamid Reza Pourghasemi ◽  
Mohammad Zare ◽  
Thomas Blaschke
Keyword(s):  
Data Mining ◽  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Northern Iran ◽  
Data Mining Algorithms ◽  
Data Volume ◽  
Mining Algorithms

The aim of this study was to apply data mining algorithms to produce a landslide susceptibility map of the national-scale catchment called Bandar Torkaman in northern Iran. As it was impossible to directly use the advanced data mining methods due to the volume of data at this scale, an intermediate approach, called normalized frequency-ratio unique condition units (NFUC), was devised to reduce the data volume. With the aid of this technique, different data mining algorithms such as fuzzy gamma (FG), binary logistic regression (BLR), backpropagation artificial neural network (BPANN), support vector machine (SVM), and C5 decision tree (C5DT) were employed. The success and prediction rates of the models, which were calculated by receiver operating characteristic curve, were 0.859 and 0.842 for FG, 0.887 and 0.855 for BLR, 0.893 and 0.856 for C5DT, 0.891 and 0.875 for SVM, and 0.896 and 0.872 for BPANN that showed the highest validation rates as compared with the other methods. The proposed approach of NFUC proved highly efficient in data volume reduction, and therefore the application of computationally demanding algorithms for large areas with voluminous data was feasible.

GIS-Based Logistic Regression for Landslide Susceptibility Analysis in Western Washington State

2017 ◽  
Vol 8 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Lucas A. Dailey ◽  
Sven Fuhrmann
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Puget Sound ◽  
Washington State ◽  
Susceptibility Map ◽  
The North ◽  
Susceptibility Maps ◽  
Western Washington ◽  
Landslide Susceptibility Maps ◽  
Puget Sound Region

The Oso landslide, one of the most recent disasters, occurred on March 22nd, 2014 in western Washington State. It caused significant property damage and killed over 40 people. As a result, a renewed interest has emerged for creating more accurate landslide susceptibility maps for this region. Research addressing landslide susceptibility within the north Puget Sound region of western Washington is lacking; therefore, this study develops a probabilistic GIS-based landslide susceptibility model for the north Puget Sound region. Multivariate logistic regression was utilized to create a landslide susceptibility map of Whatcom, Skagit, Snohomish, and King Counties. To predict probable areas of landslide occurrence, a landslide inventory map was prepared and fourteen topographic, geologic, environmental, and climatic predictor variables were considered. This research aims to assist in restructuring western Washington's landslide policies, and could serve as the first step in producing more accurate landslide susceptibility maps for the region.

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.

scholarly journals Assessing the quality of landslide susceptibility maps – case study Lower Austria

2013 ◽  
Vol 1 (2) ◽  
pp. 1001-1050 ◽  
Author(s):  
H. Petschko ◽  
A. Brenning ◽  
R. Bell ◽  
J. Goetz ◽  
T. Glade
Keyword(s):  
Confidence Intervals ◽  
Landslide Susceptibility ◽  
Spatial Planning ◽  
Cross Validation ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
High Interest ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Landslide susceptibility maps are helpful tools to identify areas which might be prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, the quality of the landslide susceptibility map and of the model applied to compute them is of high interest. In this study we focus on the analysis of the model performance by a repeated k-fold cross-validation with spatial and random subsampling. Furthermore, the focus is on the analysis of the implications of uncertainties expressed by confidence intervals of model predictions. The cross-validation performance assessments reflects the variability of performance estimates compared to single hold-out validation approaches that produce only a single estimate. The analysis of the confidence intervals shows that in 85% of the study area, the 95% confidence limits fall within the same susceptibility class. However, there are cases where confidence intervals overlap with all classes from the lowest to the highest class of susceptibility to landsliding. Locations whose confidence intervals intersect with more than one susceptibility class are of high interest because this uncertainty may affect spatial planning processes that are based on the susceptibility level.

scholarly journals Producing landslide susceptibility maps by applying expert knowledge in a GIS - based environment.

2016 ◽  
Vol 47 (3) ◽  
pp. 1539 ◽  
Author(s):  
P. Tsangaratos ◽  
D. Rozos
Keyword(s):  
Landslide Susceptibility ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Visual Basic ◽  
Susceptibility Map ◽  
Engineering Systems ◽  
Landslide Susceptibility Map ◽  
Related Factors ◽  
Susceptibility Maps ◽  
Analytic Hierarchical Process

In this paper two semi - quantative approaches, from the domain of Multi criteria decision analysis, such as Rock Engineering Systems (RES) and Analytic Hierarchical Process (AHP) are implemented for weighting and ranking landslide related factors in an objective manner. Through the use of GIS these approaches provide a highly accurate landslide susceptibility map. For this purpose and in order to automate the process, the Expert Knowledge for Landslide Assessment Tool (EKLATool) was developed as an extension tightly integrated in the ArcMap environment, using ArcObjects and Visual Basic script codes. The EKLATool was implemented in an area of Xanthi Prefecture, Greece, where a spatial database of landslide incidence was  available

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