Application of a Statistical Approach to Landslide Susceptibility Map Generation in Urban Settings (original) (raw)
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GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy
Natural Hazards and Earth …, 2010
This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.
Statistical Analysis of Landslide Susceptibility, Macerata Province (Central Italy)
Hydrology
Every year, institutions spend a large amount of resources to solve emergencies generated by hydrogeological instability. The identification of areas potentially subject to hydrogeological risks could allow for more effective prevention. Therefore, the main aim of this research was to assess the susceptibility of territories where no instability phenomena have ever been detected. In order to obtain this type of result, statistical assessments of the problem cannot be ignored. In this case, it was chosen to analyse the susceptibility to landslide using a flexible method that is attracting great interest in the international scientific community, namely the Weight of Evidence (WoE). This model-building procedure, for calculating landslide susceptibility, used Geographic Information Systems (GIS) software by means of mathematical operations between rasters and took into account parameters such as geology, acclivity, land use, average annual precipitation and extreme precipitation event...
Natural Hazards, 2008
In this article, the results of a study aimed to assess the landslide susceptibility in the Calaggio Torrent basin (Campanian Apennines, southern Italy) are presented. The landslide susceptibility has been assessed using two bivariate-statistics-based methods in a GIS environment. In the first method, widely used in the existing literature, weighting values (Wi) have been calculated for each class of the selected causal factors (lithology, land-use, slope angle and aspect) taking into account the landslide density (detachment zones + landslide body) within each class. In the second method, which is a modification of the first method, only the landslide detachment zone (LDZ) density has been taken into account to calculate the weighting values. This latter method is probably characterized by a major geomorphological coherence. In fact, differently from the landslide bodies, LDZ must necessarily occur in geoenvironmental classes prone to failure. Thus, the calculated Wi seem to be more reliable in estimating the propensity of a given class to generate failure. The thematic maps have been reclassified on the basis of the calculated Wi and then overlaid, with the purpose to produce landslide susceptibility maps. The used methods converge both in indicating that most part of the study area is characterized by a high–very high landslide susceptibility and in the location and extent of the low-susceptible areas. However, an increase of both the high–very high and moderate–high susceptible areas occurs in using the second method. Both the produced susceptibility maps have been compared with the geomorphological map, highlighting an excellent coherence which is higher using method-2. In both methods, the percentage of each susceptibility class affected by landslides increases with the degree of susceptibility, as expected. However, the percentage at issue in the lowest susceptibility class obtained using method-2, even if low, is higher than that obtained using method-1. This suggests that method-2, notwithstanding its major geomorphological coherence, probably still needs further refinements.
Quaternary International, 2007
The proposed experimental study is aimed at contributing to the landslide susceptibility evaluation using a multidisciplinary approach: geological, geomorphological and geo-engineering survey, together with multivariate statistical analysis and GIS technique. It is included in a wider research project, aimed at defining the landslide hazard in the area of the map no. 250 ''Castelnuovo di Garfagnana'' (1:50 000 scale). This study is based on the realization of a landslides inventory map and statistical data analysis using probabilistic methods.
2003 Seattle Annual Meeting, 2003
The aim of present work is the assessment and mapping of landslide susceptibility in a test area located in Cilento National Park (more precisely in Caselle in Pittari, situated in Campania, a region in Southern Italy). From the geologic point of view, this area of study is characterized by the outcrop of two stratigraphic-structural units, both deformed during Alpine orogenesis: the Alburno-Cervati unit (cretaceous), resulted from deformation of the central portion of the Campano-Lucanian carbonate platform, and the North-Calabrian unit (cenozoic) resulted from the deformation of sin-orogenic basin. The first unit is constituted by limestone formations and the second one by clayey, sandy, carbonate flysch. We studied the distribution and the characterization of landslides in the study area through photointerpretation and geomorphologic survey. This research shows that in the above mentioned area there are two types of landslides: slow earth flow and rock falls. Slow earth flow are numerous in the flysch formations and their landslide masses are quite large. Rock falls occur in limestone formations. They are less frequent and do not reach vast proportions. GIS-based techniques are the best approach to the study of landslide susceptibility because they allow the management of several themes concerning instability factors. Besides, morphometric characteristics, playing an important role in landsliding processes, can be determined through the analysis of the digital terrain model. Here follows a list of the main phases of our analysis focusing on the mapping of earth flow susceptibility: -selection of the most useful instability factors (i.e.: lithology, slope, land use, hydrography, etc.); -multivariate statistic analysis of the selected factors in landslide areas; -creation of a model of susceptibility; -mapping of landslide susceptibility; -testing phase. This method is based on the concept that instability factors in susceptible areas are similar to those observed in areas in which landslides have already happened. The application of this method would be useful to locate potential unstable areas, and so to predict the occurrence of future landslides.
Landslides, 2010
Statistical and deterministic methods are widely used in geographic information system based landslide susceptibility mapping. This paper compares the predictive capability of three different models, namely the Weight of Evidence, the Fuzzy Logic and SHALSTAB, for producing shallow earth slide susceptibility maps, to be included as informative layers in land use planning at a local level. The test site is an area of about 450 km2 in the northern Apennines of Italy where, in April 2004, rainfall combined with snowmelt triggered hundreds of shallow earth slides that damaged roads and other infrastructure. An inventory of the landslides triggered by the event was obtained from interpretation of aerial photos dating back to May 2004. The pre-existence of mapped landslides was then checked using earlier aerial photo coverage. All the predictive models were run on the same set of geo-environmental causal factors: soil type, soil thickness, land cover, possibility of deep drainage through the bedrock, slope angle, and upslope contributing area. Model performance was assessed using a threshold-independent approach (the ROC plot). Results show that global accuracy is as high as 0.77 for both statistical models, while it is only 0.56 for SHALSTAB. Besides the limited quality of input data over large areas, the relatively poorer performance of the deterministic model maybe also due to the simplified assumptions behind the hydrological component (steady-state slope parallel flow), which can be considered unsuitable for describing the hydrologic behavior of clay slopes, that are widespread in the study area.
2008
Lo scopo di questo lavoro è quello di dare un contributo sperimentale alla valutazione della pericolosità geologica connessa all'instabilità dei versanti, in un'area della media valle del Fiume Serchio. Le particolari caratteristiche geologico-strutturali, geomorfologiche, meteo-climatiche e sismiche di questa regione causano un'alta densità di dissesto. Questo studio ricade in un più ampio progetto promosso dalla Regione Toscana e dall'APAT-Servizio Geologico Nazionale con lo scopo di definire la pericolosità di frana all'interno dell'area del foglio 250-Castelnuovo di Garfagnana (scala 1:50.000). Attraverso lo studio geomorfologico di dettaglio è stato possibile mettere in evidenza i processi morfogenetici e le forme connessi con l'instabilità e realizzare una carta inventario dei fenomeni franosi. Lo studio delle caratteristiche geologiche e litologico-tecniche ha permesso di caratterizzare il substrato roccioso e le coperture dal punto di vista delle loro proprietà fisico-meccaniche. Il lavoro di zonazione dei versanti sulla base della predisposizione al dissesto si è svolto per passi successivi. Inizialmente è stato identificato un numero limitato di fattori predisponenti, relativi alle caratteristiche litologico-tecniche delle formazioni, all'acclività ed esposizione dei versanti, alla distanza dai corsi d'acqua ed ai principali lineamenti tettonici. Successivamente, l'analisi spaziale dei fattori predisponenti al dissesto, organizzati in un set di layer diversificati e l'applicazione di metodologie statistiche hanno permesso di valutare quantitativamente le interrelazioni esistenti tra i fattori predisponenti ed i fenomeni franosi. In questo studio sono stati applicati due distinti metodi statistici, entrambi di tipo indiretto e quantitativo con l'obiettivo di giungere ad una zonazione del territorio in aree con gradi di propensione al dissesto, o pericolosità relativa, diversificati.
Earth ArXiv preprint, 2022
Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space. This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to prepare slope-unit based landslide susceptibility maps. These maps are prepared for the eight types of mass movements existing in the inventory, (Complex, Deep Seated Gravitational Slope Deformation, Diffused Fall, Fall, Rapid Flow, Shallow, Slow Flow, Translational) and build one susceptibility map for each type. The analysis-carried out by using a Bayeian version of a Generalized Additive Model with a multiple intercept for each Italian region-revealed that the inventory may have been compiled with different levels of detail. This would be consistent with the datases being assembled from twenty sub-inventories, each prepared by different administrations of the Italian regions. As a result, this spatial inhonomegenity may lead to a biased national-scale susceptibility maps. On the basis of these considerations, we further analyzed the national database to confirm or reject the varying quality hypothesis suggested by the multiple intercepts results. For each landslide type, we then tried to build unbiased susceptibility models by removing regions with a poor landslide inventory from the calibration stage, and used them only as a prediction target of a simulation routine.
Earth-Science Reviews, 2022
Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space. This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to prepare slope-unit based landslide susceptibility maps. These maps are prepared for the eight types of mass movements existing in the inventory, (Complex, Deep Seated Gravitational Slope Deformation, Diffused Fall, Fall, Rapid Flow, Shallow, Slow Flow, Translational) and we build one susceptibility map for each type. The analysis – carried out by using a Bayesian version of a Generalized Additive Model with a multiple intercept for each Italian region – revealed that the inventory may have been compiled with different levels of detail. This would be consistent with the dataset being assembled from twenty sub–inventories, each prepared by different administrations of the Italian regions. As a result, this spatial heterogeneity may lead to biased national–scale susceptibility maps. On the basis of these considerations, we further analyzed the national database to confirm or reject the varying quality hypothesis on the basis of the model equipped with multiple regional intercepts. For each landslide type, we then tried to build unbiased susceptibility models by removing regions with a poor landslide inventory from the calibration stage, and used them only as a prediction target of a simulation routine. We analyzed the resulting eight maps finding out a congruent dominant pattern in the Alpine and Apennine sectors. The whole procedure is implemented in R–INLA. This allowed to examine fixed (linear) and random (nonlinear) effects from an interpretative standpoint and produced a full prediction equipped with an estimated uncertainty. We propose this overall modeling pipeline for any landslide datasets where a significant mapping bias may influence the susceptibility pattern over space.
Rendiconti online della Società Geologica Italiana, 2015
Two methodologies for landslide susceptibility mapping in a Geographical Information System (GIS) environment are here presented. Camaldoli hill, the most prominent peak of the Phlegrean district (458 m a.s.l.), was selected for the model implementation. To this aim, bi-and multivariate approaches were adopted, considering seven possible factors predisposing to landslide occurrence (geology, slope, aspect, land cover, distance to streams, rocky scarps and distance to roads). The susceptibility maps produced with the two methods were then compared and critically evaluated using validation datasets (Receiver Operating Characteristic -ROC curves).