90 research outputs found
UTILIZZO DEL SISTEMA GOOGLE EARTH PER LA DEFINIZIONE DI UN MODELLO DI SUSCETTIBILITĂ€ DA FRANA: UN TEST IN SICILIA CENTRALE
Exploiting Google EarthTM to assess a landslide susceptibility model: a test in central Sicily. A landslide susceptibility multivariate model, based on the conditional analysis approach, has been derived in the Tumarrano river basin (about 78 km2), by intersecting a GIS grid layer, expressing some selected geo-environmental conditions (outcropping lithology, steepness, plan curvature and topographic wetness index), and a landslide vector archive, produced by a Google EarthTM aided remote survey. The analysis of the Google EarthTM images dated at 2006, allowed to recognize 733 landslides (30 rotational slides and 703 flows), almost exclusively affecting clay and sandy clay rocks. Validation procedures produced largely satisfactory results, which were analyzed in the domain of the success and prediction rate curves. The research confirms the goodness of the susceptibility assessment method, as well as the powerful of Google EarthTM as a tool to manage the need of new, detailed and multi-temporal landslide archives
Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)
Forward logistic regression has allowed us to derive an
earth-flow susceptibility model for the Tumarrano river basin,
which was defined by modeling the statistical relationships between
an archive of 760 events and a set of 20 predictors. For each
landslide in the inventory, a landslide identification point (LIP)
was automatically produced as corresponding to the highest point
along the boundary of the landslide polygons, and unstable conditions
were assigned to cells at a distance up to 8m. An equal
number of stable cells (out of landslides) was then randomly
extracted and appended to the LIPs to prepare the dataset for
logistic regression. A model building strategy was applied to enlarge
the area included in training the model and to verify the
sensitivity of the regressed models with respect to the locations of
the selected stable cells. A suite of 16 models was prepared by
randomly extracting different unoverlapping stable cell subsets
that have been appended to the unstable ones. Models were finally
submitted to forward logistic regression and validated. The results
showed satisfying and stable error rates (0.236 on average, with a
standard deviation of 0.007) and areas under the receiver operating
characteristic (ROC) curve (AUCs) (0.839 for training and
0.817 for test datasets) as well as factor selections (ranks and
coefficients). As regards the predictors, steepness and large-profile
and local-plan topographic curvatures were systematically selected.
Clayey outcropping lithology, midslope drainage, local and
midslope ridges, and canyon landforms were also very frequently
(from eight to 15 times) included in the models by the forward
selection procedures. The model-building strategy allowed us to
produce a performing earth-flow susceptibility model, whose model
fitting, prediction skill, and robustness were estimated on the basis of
validation procedures, demonstrating the independence of the
regressed model on the specific selection of the stable cells
Exploring the geomorphological adequacy of the landslide susceptibility maps: A test for different types of landslides in the Bidente river basin (northern Italy)
Landslide susceptibility modelling is a crucial tool for implementing effective strategies in landslide risk mitigation. A plethora of statistical methods is available for generating accurate prediction images; however, the reliability of these models in terms of geomorphological adequacy is often overlooked by scholars. This critical flaw may result in concealed prediction errors, undermining the trustworthiness of the obtained maps. A key aspect of evaluating the geomorphological soundness of these models lies in factor analysis, specifically considering the correlation of explanatory variables with the final susceptibility score rather than solely focusing on their impact on model accuracy. This study delves into research conducted in the Bidente river basin (Italy) that analyes results obtained from slide, flow, and complex susceptibility models using Weight of Evidence (WoE) and Multivariate Adaptive Regression Splines (MARS) statistical methods. The research critically examines each factor class's role in defining susceptibility scores for different landslide typologies. The comparison between susceptibility maps generated by WoE and MARS for each typology (slide = 0.78; flow = 0.85; complex: 0.79) (slide = 0.78; flow = 0.85; complex: 0.79)reveals good to excellent prediction skill, with MARS demonstrating a 5 % higher performance index. The study emphasises the importance of spatial relationships between variables and landslide occurrences, highlighting that individual classes of variables influence the final susceptibility score based on their combined role with other predictor classes. In particular, in this study, results highlight that lithotecnical and landform classification classes delimit the landslide domain, while topographic attributes (steepness, curvatures, SPI and TWI) modulate the score inside. The proposed approach offers insights into investigating the geomorphological adequacy of landslide prediction images, emphasising the significance of factor analysis in evaluating model reliability and uncovering potential errors in susceptibility maps
Evaluation of debris flow susceptibility in El Salvador (CA): a comparison between Multivariate Adaptive Regression Splines (MARS) and Binary Logistic Regression (BLR)
In the studies of landslide susceptibility assessment which have been developed in recent years, statistical methods have increasingly been applied. Among all, the BLR (Binary Logistic Regression) certainly finds a more extensive application while MARS (Multivariate Adaptive Regression Splines), despite the good performance and the innovation of the strategies of analysis, only recently began to be employed as a statistical tool for predicting landslide occurrence. The purpose of this research was to evaluate the predictive performance and identify possible drawbacks of the two statistical techniques mentioned above, focusing in particular on the prediction of debris flows. To this aim, we employed an inventory of debris flows triggered by the passage of the hurricane IDA and the low-pressure system associated with it 96E, on November 7thand 8th2009 in the Caldera Ilopango, El Salvador (CA). Two validation strategies have been applied to both statistical techniques thus obtaining four models (BLR(I), MARS(I), BLR(II), MARS(II)) to be compared in pairs. Model performance was assessed in terms of AUC (area under the receiver operating characteristic (ROC) curve), Sensitivity, Specificity, Positive Prediction Value and Negative Prediction Value. Moreover, to evaluate the robustness of the modeling procedure, 50 replicates were created for each model and the standard deviation was calculated for each of them. The results show that both techniques allow for obtaining good or excellent performances so that it is not possible to define one of the two techniques as absolutely better. However, the validation procedure reveals slightly better performance of the MARS models, with greater sensitivity and greater discrimination among TNs
Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)
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.This research was supported by project CGL2008-04854 funded by the Ministry of Science and Education of Spain and was developed in the RNM121 Research
Group funded by the Andalusian Research Plan
Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA)
In this study, an inventory of storm-triggered debris flows performed in the area of the San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck the area in November 2009 as the result of the simultaneous action of low-pressure system 96E and Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows. Validation of the models was performed by splitting 100 random samples of event and non-event 10 m pixels into training and test subsets. The validation results revealed an excellent (area under the receiver operating characteristic (ROC) curve (AUC) = 0.80) and stable (AUC std. dev. = 0.01) ability of MARS to predict the locations of the debris flows which occurred in the study area. However, when using the Youden’s index as probability threshold to discriminate between pixels predicted as positives and negatives, MARS exhibits a moderate ability to identify stable cells (specificity = 0.66). The final debris flow susceptibility map, which was prepared by averaging for each pixel the score of the 100 MARS repetitions, shows where future debris flows are more likely to occur, and thus may help in mitigating the risk associated with these landslides
Integrazione di metodologie geofisiche per lo studio di problemi idrogeologici e geromorfologici: due esempi applicativi
Dottorato di ricerca in geofisica per l'ambiente ed il territorio. 12. ciclo. A.a. 1998-99. Tutore Dario Luzio. Cotutore Valerio Agnesi. Coordinatore Antonio BottariConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - Piazza Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal
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