Web data mining for automatic inventory of geohazards at national scale

In this study, we present a fully automated procedure to analyze online news using data mining techniques. It is then used to compile and continually update a geohazard database. The procedure is based on new technologies that publish news on the internet, i.e., the news is analyzed, georeferenced and attributed to a category of geohazards (the current categories are landslides, floods and earthquakes). A continuous flow of georeferenced events is established to populate and update the geodatabase automatically and in near-real time. We tested the procedure for 2 years at a national scale, creating a geodatabase containing more than 20,000 news items concerning geohazards that occurred in Italy. This procedure enables continuous feedback from events in the real world, such that information about geohazards can be fully exploited rapidly (compared to traditional techniques based on remote sensing, field surveys and historical inventories).     

Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

Landslide inventory maps are necessary for assessing landslide hazards and addressing the role slope stability plays in landscape evolution over geologic timescales. However, landslide inventory maps produced with traditional methods — aerial photograph interpretation, topographic map analysis, and field inspection — are often subjective and incomplete. The increasing availability of high-resolution topographic data acquired via airborne Light Detection and Ranging (LiDAR) over broad swaths of terrain invites new, automated landslide mapping procedures. We present two methods of spectral analysis that utilize LiDAR-derived digital elevation models of the Puget Sound lowlands, Washington, and the Tualatin Mountains, Oregon, to quantify and automatically map the topographic signatures of deep-seated landslides. Power spectra produced using the two-dimensional discrete Fourier transform and the two-dimensional continuous wavelet transform identify the characteristic spatial frequencies of deep-seated landslide morphologic features such as hummocky topography, scarps, and displaced blocks of material. Spatial patterns in the amount of spectral power concentrated in these characteristic frequency bands highlight past slope instabilities and allow the delineation of landslide terrain. When calibrated by comparison with detailed, independently compiled landslide inventory maps, our algorithms correctly classify an average of 82% of the terrain in our five study areas. Spectral analysis also allows the creation of dominant wavelength maps, which prove useful in analyzing meter-scale topographic expressions of landslide mechanics, past landslide activity, and landslide-modifying geomorphic processes. These results suggest that our automated landslide mapping methods can create accurate landslide maps and serve as effective, objective, and efficient tools for digital terrain analysis.     

云南滑坡泥石流灾害的气象成因与监测

根据云南滑坡泥石流灾害资料，分析了其形成环境与分布特征，给出了典型降水滑坡泥石流灾害事件，研究了滑坡泥石流灾害与气象环境的关系。云南滑坡泥石流灾害主要出现在盛夏7～8月，主要影响天气系统有切变线、冷锋、西风槽、西南涡、孟加拉湾风暴、南海西行台风和两高辐合区。诱发滑坡泥石流灾害的前期降水类型有3种，即暴雨型，中～大雨型和连阴雨型。得出了云南滑坡泥石流发生的区域临界雨量指标，提出了滑坡泥石流灾害的气象监测预警方法。     

引发意大利托斯卡纳区滑坡的区域降水阈值体系的定义——统计与环境分析(英文)

The aim of this work is the determination of regional-scale rainfall thresholds for the triggering of landslides in the Tuscany Region(Italy).The critical rainfall events related to the occurrence of 593 past landslides were characterized in terms of duration(D) and intensity(I).I and D values were plotted in a log-log diagram and a lower boundary was clearly noticeable:it was interpreted as a threshold representing the rainfall conditions associated to landsliding.That was also confirmed by a comparison with many literature thresholds,but at the same time it was clear that a similar threshold would be affected by a too large approximation to be effectively used for a regional warning system.Therefore,further analyses were performed differentiating the events on the basis of seasonality,magnitude,location,land use and lithology.None of these criteria led to discriminate among all the events different groups to be characterized by a specific and more effective threshold.This outcome could be interpreted as the demonstration that at regional scale the best results are obtained by the simplest ap-proach,in our case an empirical black box model which accounts only for two rainfall pa-rameters(I and D).So a set of thresholds could be conveniently defined using a statistical approach:four thresholds corresponding to four severity levels were defined by means of the prediction interval technique and we developed a prototype warning system based on rainfall recordings or weather forecasts.     

Comparing landslide inventory maps

Landslide inventory maps are effective and easily understandable products for both experts, such as geomorphologists, and for non experts, including decision-makers, planners, and civil defense managers. Landslide inventories are essential to understand the evolution of landscapes, and to ascertain landslide susceptibility and hazard. Despite landslide maps being compiled every year in the word at different scales, limited efforts are made to critically compare landslide maps prepared using different techniques or by different investigators. Based on the experience gained in 20 years of landslide mapping in Italy, and on the limited literature on landslide inventory assessment, we propose a general framework for the quantitative comparison of landslide inventory maps. To test the proposed framework we exploit three inventory maps. The first map is a reconnaissance landslide inventory prepared for the Umbria region, in central Italy. The second map is a detailed geomorphological landslide map, also prepared for the Umbria region. The third map is a multi-temporal landslide inventory compiled for the Collazzone area, in central Umbria. Results of the experiment allow for establishing how well the individual inventories describe the location, type and abundance of landslides, to what extent the landslide maps can be used to determine the frequency-area statistics of the slope failures, and the significance of the inventory maps as predictors of landslide susceptibility. We further use the results obtained in the Collazzone area to estimate the quality and completeness of the two regional landslide inventory maps, and to outline general advantages and limitations of the techniques used to complete the inventories.     

Extracting and analyzing semantic relatedness between cities using news articles

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.     

Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

During the last decade, slope failures were reported in a 500 km² study area in the Geba–Werei catchment, northern Ethiopia, a region where landslides were not considered an important hazard before. Field observations, however, revealed that many of the failures were actually reactivations of old deep-seated landslides after land use changes. Therefore, this study was conducted (1) to explore the importance of environmental factors controlling landslide occurrence and (2) to estimate future landslide susceptibility. A landslide inventory map of the study area derived from aerial photograph interpretation and field checks shows the location of 57 landslides and six zones with multiple landslides, mainly complex slides and debris flows. In total 14.8% of the area is affected by an old landslide. For the landslide susceptibility modelling, weights of evidence (WofE), was applied and five different models were produced. After comparison of the models and spatial validation using Receiver Operating Characteristic curves and Kappa values, a model combining data on elevation, hillslope gradient, aspect, geology and distance to faults was selected. This model confirmed our hypothesis that deep-seated landslides are located on hillslopes with a moderate slope gradient (i.e. 5°–13°). The depletion areas are expected on and along the border of plateaus where weathered basalts rich in smectite clays are found, and the landslide debris is expected to accumulate on the Amba Aradam sandstone and upper Antalo limestone. As future landslides are believed to occur on inherently unstable hillslopes similar to those where deep-seated landslides occurred, the classified landslide susceptibility map allows delineating zones where human interventions decreasing slope stability might cause slope failures. The results obtained demonstrate that the applied methodology could be used in similar areas where information on the location of landslides is essential for present-day hazard analysis.     

GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study

This study describes the assessment of landslide susceptibility in Sicily (Italy) at a 1:100,000 scale using a multivariate logistic regression model. The model was implemented in a GIS environment by using the ArcSDM (Arc Spatial Data Modeller) module, modified to develop spatial prediction through regional data sets. A newly developed algorithm was used to automatically extract the detachment area from mapped landslide polygons. The following factors were selected as independent variables of the logistic regression model: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. The above-described configuration has been verified to be the best one among others employing from three to eight factors. All the regression coefficients and parameters were calculated using selected landslide training data sets. The results of the analysis were validated using an independent landslide data set. On an average, 82% of the area affected by instability and 79% of the not affected area were correctly classified by the model, which proved to be a useful tool for planners and decision-makers.     

GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy)

Landslide hazard assessment, effected by means of geostatistical methods, is based on the analysis of the relationships between landslides and the spatial distributions of some instability factors. Frequently such analyses are based on landslide inventories in which each record represents the entire unstable area and is managed as a single instability landform. In this research, landslide susceptibility is evaluated through the study of a variety of instability landforms: landslides, scarps and areas uphill from crown. The instability factors selected were: bedrock lithology, steepness, topographic wetness index and stream power index. The instability landform densities computed for all the factors, which were arranged in Unique Condition Unit, allowed us to derive a total of three prediction images for each landslide typology. The role of the instability factors and the effects generated by the use of different landforms were analyzed by means of: a) bivariate analysis of the relationships between factors and landslide density; b) predictive power validations of the prediction images, based on a random partition strategy.The test area was the Iato River Basin (North-Western Sicily), whose slopes are moderately involved in flow and rotational slide landslides (219 and 28, respectively). The area is mainly made up of the following complexes: Numidian Flysch clays (19%, 1%), Terravecchia sandy clays (5%, 1%), Terravecchia clayey sands (3%, 0.3%) and San Cipirello marly clays (9%, 0%). The steepness parameter shows the highest landslide density in the [11–19°] class for both the typologies (8%, 1%), even if the density distributions for rotational slides are right-asymmetric and right-shifted. We obtained significant differences in shape when we used different instability landforms. Unlike scarps and areas uphill from crowns, landslide areas produce left-asymmetric and left-shifted density distributions for both the typologies. As far as the topographic wetness index is concerned, much more pronounced differences were detected among the instability landforms of rotational slides. In contrast, the flow landslides produce normal-like density distributions. The latter and the rotational slide landslide areas produce the highest density values in the class [5.5–6.7], despite an abrupt decreasing trend starting from the first class [3.2–4.4], which is generated by the density values of the rotational slide scarps and areas uphill from crowns. The stream power index at the foot of the slopes, which was automatically derived using a GIS-procedure, shows a positive correlation with the landslide densities marked by the maximum classes: [4.8–6.0] for flows, and [6.0–7.2] for rotational slides. The validation procedure results confirmed that the choice of instability landform influences the results of the susceptibility analysis. Furthermore, the validation procedure indicates that: a) the predictive models are generally satisfactory; b) scarps and zones uphill from crown areas are the most diagnostically unstable landforms, for flow and rotational slide landslides respectively.     

Probabilistic landslide hazard assessment at the basin scale

We propose a probabilistic model to determine landslide hazard at the basin scale. The model predicts where landslides will occur, how frequently they will occur, and how large they will be. We test the model in the Staffora River basin, in the northern Apennines, Italy. For the study area, we prepare a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs taken between 1955 and 1999. We partition the basin into 2243 geo-morpho-hydrological units, and obtain the probability of spatial occurrence of landslides by discriminant analysis of thematic variables, including morphological, lithological, structural and land use. For each mapping unit, we obtain the landslide recurrence by dividing the total number of landslide events inventoried in the unit by the time span of the investigated period. Assuming that landslide recurrence will remain the same in the future, and adopting a Poisson probability model, we determine the exceedance probability of having one or more landslides in each mapping unit, for different periods. We obtain the probability of landslide size by analysing the frequency–area statistics of landslides, obtained from the multi-temporal inventory map. Assuming independence, we obtain a quantitative estimate of landslide hazard for each mapping unit as the joint probability of landslide size, of landslide temporal occurrence and of landslide spatial occurrence.     

Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry

A map of extant slope failures is the most basic element of any landslide assessment. Without an accurate inventory of slope instability, it is not possible to analyze the controls on the spatial and temporal patterns of mass movement or the environmental, human, or geomorphic consequences of slides. Landslide inventory maps are tedious to compile, difficult to make in vegetated terrain using conventional techniques, and tend to be subjective. In addition, most landslide inventories simply outline landslide boundaries and do not offer information about landslide mechanics as manifested by internal deformation features. In an alternative approach, we constructed accurate, high-resolution DEMs from airborne laser altimetry (LIDAR) data to characterize a large landslide complex and surrounding terrain near Christchurch, New Zealand. One-dimensional, circular (2-D) and spherical (3-D) statistics are used to map the local topographic roughness in the DEMs over a spatial scale of 1.5 to 10 m. The bedrock landslide is rougher than adjacent unfailed terrain and any of the statistics can be employed to automatically detect and map the overall slide complex. Furthermore, statistics that include a measure of the local variability of aspect successfully delineate four kinematic units within the gently sloping lower half of the slide. Features with a minimum size of surface folds that have a wavelength of about 11 to 12 m and amplitude of about 1 m are readily mapped. Two adjacent earthflows within the landslide complex are distinguished by a contrast in median roughness, and texture and continuity of roughness elements. The less active of the earthflows has a surface morphology that presumably has been smoothed by surface processes. The Laplacian operator also accurately maps the kinematic units and the folds and longitudinal levees within and at the margins of the units. Finally, two-dimensional power spectra analyses are used to quantify how roughness varies with length scale. These results indicate that no dominant length scale of roughness exists for smooth, unfailed terrain. In contrast, zones with different styles of landslide deformation exhibit distinctive spectral peaks that correspond to the scale of deformation features, such as the compression folds. The topographic-based analyses described here may be used to objectively delineate landslide features, generate mechanical inferences about landslide behavior, and evaluate relatively the recent activity of slides.     

Landslide triggering scenarios in homogeneous geological contexts: The area surrounding Acri (Calabria, Italy)

The paper describes a methodology to detect landslide triggering scenarios in geological homogeneous areas and for some specific landslide categories. In these scenarios, the rainfall–landslide relationship as well as the pluviometric load conditions influencing slope instability have to be investigated.The methodology is applied to an area located in northern Calabria (Italy) and affected by widespread and different slope instability phenomena. Outcropped, fractured, and deeply weathered crystalline rock masses, determining geologic homogeneous conditions, are present. In the same area, suitable and homogeneous climatic features have also been found.According to the methodology adopted, the hydrologic analysis of rainfall time-series is initially carried out notwithstanding historical data concerning landslide mobilization, but using simple models to determine critical pluviometric scenarios for the three landslide categories: shallow, medium-deep, and deep. Landslide-triggering scenarios individualized according to this procedure are less significant as compared to the landslide mobilization detected in the study area by means of historical research and ascribed to the three landslide categories according to geomorphologic analysis.Subsequently, the possible landslide triggering scenarios are outlined by carefully investigating the hydrologic analysis limited to the periods identified according to the historical data.In the study area and approximately for all the areas characterized by the outcrop of fractured and deeply weathered crystalline rocks, significant triggering scenarios can be outlined. In particular, shallow landslide triggers could be activated by rainfall events with intensities exceeding 90 mm/day and/or with amounts exceeding 160 mm. As for medium-deep and deep landslides, triggering mechanisms are more complicated; and effective rainfall contribution must be taken into account compared to groundwater storage. Moreover, a more complex link between deep landslides and precipitation is confirmed.The results obtained to date highlight the potential of this methodology, which enables us to define and progressively improve the knowledge framework by means of a work sequence integrating different disciplinary tools and results.     

The use of surface monitoring data for the interpretation of landslide movement patterns

The Tessina landslide is a large, seasonally active slope failure located on the southern slopes of Mt. Teverone, in the Alpago valley of NE Italy, consisting of a complex system that has developed in Tertiary Flysch deposits. The landslide, which first became active in 1960, threatens two villages and is hence subject to detailed monitoring, with high quality data being collected using piezometers, inclinometers, extensometers, and through the use of a highly innovative, automated Electronic Distance Measurement (EDM) system, which surveys the location of a large number of reflector targets once every 6 h. These systems form the basis of a warning system that protects the villages, but they also provide a very valuable insight into the patterns of movement of the landslide.In this paper, analysis is presented of the movement of the landslide, concentrating on the EDM dataset, which provides a remarkable record of surface displacement patterns. It is proposed that four distinct movement patterns can be established, which correspond closely to independently defined morphological assessments of the landslide complex. Any given block of material transitions through the four phases of movement as it progresses down the landslide, with the style of movement being controlled primarily by the groundwater conditions. The analysis is augmented with modelling of the landslide, undertaken using the Itasca FLAC code. The modelling suggests that different landslide patterns are observed for different parts of the landslide, primarily as a result of variations in the groundwater conditions. The model suggests that when a movement event occurs, displacements occur initially at the toe of the landslide, then retrogress upslope.     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.     

论滑坡学

滑坡学可分为理论滑坡学和应用滑坡学．前者的分支有：滑坡发生学、滑坡运动学、滑坡组构学、滑坡形态学、滑坡断代－周期学、滑坡分类学、滑坡分布学、滑坡制图学、滑坡编目学和滑坡数据库系统等．应用滑坡学含滑坡研究方法论、技术手段和滑坡防御工程等．     

Representative rainfall thresholds for landslides in the Nepal Himalaya

Measuring some 2400 km in length, the Himalaya accommodate millions of people in northern India and Pakistan, Nepal, Bhutan, and parts of other Asian nations. Every year, especially during monsoon rains, landslides and related natural events in these mountains cause tremendous damage to lives, property, infrastructure, and environment. In the context of the Himalaya, however, the rainfall thresholds for landslide initiation are not well understood. This paper describes regional aspects of rainfall thresholds for landslides in the Himalaya. Some 677 landslides occurring from 1951 to 2006 were studied to analyze rainfall thresholds. Out of the 677 landslides, however, only 193 associated with rainfall data were analyzed to yield a threshold relationship between rainfall intensity, rainfall duration, and landslide initiation. The threshold relationship fitted to the lower boundary of the field defined by landslide-triggering rainfall events is I = 73.90D^− 0.79 (I = rainfall intensity in mm h^− 1 and D = duration in hours), revealing that when the daily precipitation exceeds 144 mm, the risk of landslides on Himalayan mountain slopes is high. Normalized rainfall intensity–duration relationships and landslide initiation thresholds were established from the data after normalizing rainfall-intensity data with respect to mean annual precipitation (MAP) as an index in which N_I = 1.10D^− 0.59 (N_I = normalized intensity in h^− 1). Finally, the role of antecedent rainfall in causing landslides was also investigated by considering daily rainfall during failure and the cumulative rainfall to discover at what point antecedent rainfall plays an important role in Himalayan landslide processes. Rainfall thresholds presented in this paper are generalized so they can be used in landslide warning systems in the Nepal Himalaya.     

Terrain Pattern Recognition and Spatial Decision Making for Regional Slope Stability Studies

A terrain partition scheme is presented that allows the identification of regions with high landslide risk in natural terrain zones on the basis of geomorphometric criteria from moderate resolution DEMs. The key factor being the terrain segmentation to aspect regions (regions formed by points preserving the same aspect direction) instead of using an artificial regular-grid terrain partition scheme. The study area is in western Greece (NW Peloponnesus) whereas a moderate resolution digital elevation model with spacing 75 m is used. Landslide inventory analysis and knowledge conceptualization identified that the landslide susceptibility of a particular aspect region is high, if the mean elevation is low and the mean gradient is high. Each aspect region was parametrically represented on the basis of its mean gradient and elevation. The domain of each parameter was divided to seven slices (classes) on the basis of the observed density. Subsequent knowledge based mapping identified aspect regions with high landslide susceptibility for the following spatial rule: (a) “mean slope in class 6 or 7” and (b) “mean elevation in class 1 to 5”. Alternatively the rule is expressed as mean slope to be equal or greater than 15^∘ whereas mean elevation to be in the range 0 to 750 m. These identified zones correspond to regions where historical landslides occurred (populated coastal areas in the North) as well as to south regions (natural terrain zone) where no landslide record is available, because of the limitations posed by the natural terrain landslide mapping program in Greece. The presented terrain segmentation technique combined to the spatial decision-making process, provided both an object framework for integrating geomorphometric parameters and a method for landslide risk analysis in natural terrain zones.     

滑坡危险度区划研究述评

滑坡危险度区划是滑坡研究发展到一定深度所提出的新课题。到目前为止，我国既无统一的滑坡危险度评价理论体系，也无统一的滑坡危险度区划制图方法。因此，该项工作异常薄弱，这是造成预防不及时，措施不力，从而导致广泛、严重滑坡灾害的原因之一。滑坡危险度区划的主要目的是：①建立标准化评价体系；②滑坡信息库；③评价区域滑坡的危险性；④提供标准化危险度区划图件；⑤减灾防灾的决策依据。文章对此问题的研究动态评述基础上。提出了危险度区划的内涵和研究目标。     

Susceptibility and triggering scenarios at a regional scale for shallow landslides

The work aims at identifying susceptible areas and pluviometric triggering scenarios at a regional scale in Calabria (Italy), with reference to shallow landsliding events. The proposed methodology follows a statistical approach and uses a database linked to a GIS that has been created to support the various steps of spatial data management and manipulation. The shallow landslide predisposing factors taken into account are derived from (i) the 40-m digital terrain model of the region, an  15,075 km² extension; (ii) outcropping lithology; (iii) soils; and (iv) land use. More precisely, a map of the slopes has been drawn from the digital terrain model. Two kinds of covers [prevalently coarse-grained (CG cover) or fine-grained (FG cover)] were identified, referring to the geotechnical characteristics of geomaterial covers and to the lithology map; soilscapes were drawn from soil maps; and finally, the land use map was employed without any prior processing.Subsequently, the inventory maps of some shallow landsliding events, totaling more than 30,000 instabilities of the past and detected by field surveys and photo aerial restitution, were employed to calibrate the relative importance of these predisposing factors.The use of single factors (first level analysis) therefore provides three different susceptibility maps. Second level analysis, however, enables better location of areas susceptible to shallow landsliding events by crossing the single susceptibility maps.On the basis of the susceptibility map obtained by the second level analysis, five different classes of susceptibility to shallow landsliding events have been outlined over the regional territory: 8.9% of the regional territory shows very high susceptibility, 14.3% high susceptibility, 15% moderate susceptibility, 3.6% low susceptibility, and finally, about 58% very low susceptibility.Finally, the maps of two significant shallow landsliding events of the past and their related rainfalls have been utilized to identify the relevant pluviometric triggering scenarios. By using 205 daily rainfall series, different triggering pluviometric scenarios have been identified with reference to CG and FG covers: a value of 365 mm of the total rainfall of the event and/or 170 mm/d of the rainfall maximum intensity and a value of 325 mm of the total rainfall of the event and/or 158 mm/d of the rainfall maximum intensity are able to trigger shallow landsliding events for CG and FG covers, respectively.The results obtained from this study can help administrative authorities to plan future development activities and mitigation measures in shallow landslide-prone areas. In addition, the proposed methodology can be useful in managing emergency situations at a regional scale for shallow landsliding events triggered by intense rainfalls; through this approach, the susceptibility and the pluviometric triggering scenario maps will be improved by means of finer calibration of the involved factors.