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A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In a recent paper, Mondini et al. (Nat Commun 14:2466, 2023) proposed a deep-learning system for short-term forecasting of rain-induced shallow landslides in Italy. Here, we independently evaluate the performance of this national-scale system by demonstrating its application between 1 January and 31 May 2021. For the purpose, we use hourly rainfall measurements from the same rain gauge network and different and independent information on the timing and location of 163 rain-induced landslides obtained from the FraneItalia catalogue that occurred in Italy in a period non considered in the construction of the system (https://zenodo.org/records/7923683). Independent demonstration confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also showed that the system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result as multiple landslides are inherently more dangerous than single failures. Analysis of the few misclassified landslides showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning system analysed is well suited for short-term operational forecasting of rain-induced shallow landslides in Italy. [ABSTRACT FROM AUTHOR]

The Lueyang and Xunyang counties in the Shaanxi province (China) are highly susceptible to rainfall-induced landslides. Rainfall thresholds are the most used tool to predict the occurrence of rainfall-induced landslides over large areas. However, the definition of robust thresholds may be difficult for unbalanced datasets, for which the number of non-landslide observations is much higher than the number of landslide observations. This study aims at defining adequate rainfall thresholds for the two study areas using landslide datasets that are strongly unbalanced in terms of occurrences vs. non-occurrences. Two types of rainfall thresholds are determined using a frequentist method at several non-exceedance and exceedance probabilities, separately considering rainfall events responsible for landslides (positive thresholds) and rainfall events not responsible for landslides (negative thresholds). The comparison between the two sets of thresholds shows that the method based on non-triggering events allows defining rainfall thresholds characterized by lower uncertainties and a better performance than the ones defined considering the triggering events, in both the study areas. In particular, the best-performing thresholds are the negative threshold defined at 15% exceedance probability for the Lueyang county and the negative threshold defined at 20% exceedance probability for the Xunyang county. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a procedure for assessing—at the municipal scale—the level of risk (or attention required) of stretches of roads exposed to the activity of existing or potential slow-moving landslides. This procedure can be applied to support studies of road networks over large areas aimed at the prioritization of risk-mitigation measures and the identification of road sections requiring further investigation. Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks. [ABSTRACT FROM AUTHOR]

Landslide early warning systems at regional scale are typically based on correlations between landslides that occurred in the past and rainfall monitoring data in order to identify trigger rainfall thresholds. Recently, the availability of large datasets of atmospheric measurements allows including additional variables, increasing the reliability of the models. However, conducting the analyses with traditional techniques can be quite complex and time-consuming. The purpose of this preliminary study is to demonstrate that machine learning techniques can be used to analyze monitoring data in order to select the most relevant variables for the triggering of shallow rainfall-induced landslides at regional scale. The models developed herein were tested in one of the alert zones defined by civil protection for the management of geo-hydrological risk in Campania region, Italy. Two data sources were used in the analysis. The atmospheric variables are derived from the ERA5-Land atmospheric reanalysis. The data on landslide events are retrieved from "FraneItalia", a georeferenced catalog of landslides occurred in Italy developed by consulting online sources from 2010 onwards. The models developed were calibrated and validated in order to define combinations of rainfall variables and soil water content for the prediction of the occurrence of landslides. Finally, the performance of the models was assessed using statistical indicators derived from contingency matrices. [ABSTRACT FROM AUTHOR]

Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies. [ABSTRACT FROM AUTHOR]

A methodology designed to integrate widespread meteorological monitoring and pore water pressure measurements is proposed. The procedure is tested in 30 hydrological basins highly susceptible to weather-induced landslides in Norway. The following data are used: a catalog of 125 weather-induced landslides in soils registered between January 2013 and June 2017, widespread meteorological monitoring data employed in a territorial warning model, and pore water pressure measurements retrieved from boreholes installed for a variety of geotechnical projects. The territorial warning model is initially applied to identify the warning events and the correspondent warning level in the test areas over the analysis period. Afterwards, a method for assessing the territorial warning events by analyzing the trends of the monitored pore water pressures is proposed. Finally, an augmented territorial warning model is calibrated and validated using statistical indicators widely adopted in literature. The analysis of the results reveals a satisfactory correspondence between days with landslides and the warning levels provided by the augmented territorial warning model. A final comparison between the results of the model calibration and the model validation highlighted the consistency of the model performance, once the three model parameters are adequately set. [ABSTRACT FROM AUTHOR]

The main aim of this study is the description and the analysis of the monitoring strategies implemented within local landslide early warning systems (Lo-LEWS) operational all around the world. Relevant information on 29 Lo-LEWS have been retrieved from peer-reviewed articles published in scientific journals and proceedings of technical conferences, books, reports, and institutional web pages. The first part of the paper describes the characteristics of these early warning systems considering their different components. The main characteristics of each system are summarized using tables with the aim of providing easily accessible information for technicians, experts, and stakeholders involved in the design and operation of Lo-LEWSs. The second part of the paper describes the monitoring networks adopted within the considered systems. Monitoring strategies are classified in terms of monitored activities and methods detailing the parameters and instruments adopted. The latter are classified as a function of the type of landslide being monitored. The discussion focuses on issues relevant for early warning, including appropriateness of the measurements, redundancy of monitoring methods, data analysis, and performance. Moreover, a description of the most used monitoring parameters and instruments for issuing warnings is presented. [ABSTRACT FROM AUTHOR]

The International School on "LAndslide Risk Assessment and Mitigation" (LARAM) (http://www.laram.unisa.it) was founded at the University of Salerno in 2005. The LARAM class will be composed of 40 selected PhD students and up to 10 young doctors. [Extracted from the article]

Among the many mitigation measures available for reducing the risk to life related to landslides, early warning systems certainly constitute a significant option available to the authorities in charge of risk management and governance. Two categories of landslide early warning systems (LEWSs) can be defined as a function of the scale of analysis. Systems addressing single landslides at slope scale can be named local LEWSs (Lo-LEWSs), systems operating over wide areas at regional scale are herein referred to as territorial systems (Te-LEWSs). In the literature there are several proposals schematizing the structure of LEWSs. They highlight the importance of the interconnection among different know-how and system components, as well as the key role played by the actors involved in the design and deployment of these systems. This worldwide review is organized describing and discussing the main components of 24 Te-LEWSs, following an original conceptual model based on four main tiles: setting, modelling, warning and response. Te-LEWSs are predominantly managed by governmental institutions, thus information is often difficult to find in the literature and, when available, it is not always complete and thorough. The information considered herein has been retrieved from different sources: articles published in the scientific literature, grey literature, personal contacts with system managers, and web pages. Te-LEWSs mainly deal with rainfall-induced landslides, thus pluviometers are the main monitoring instruments. Intensity duration thresholds are typically employed and meteorological modelling is often used to forecast the expected amount of rainfall in order to issue a warning with a given lead time. Public or internal statements are disseminated for increasing the preparedness of both the public and institutions or agencies. Since the beginning of the 21st century, Te-LEWSs are slowly becoming a commonly used risk mitigation option, employed worldwide, for landslide risk management over wide areas. Considerations and insights on key-points for the success or the failure of Te-LEWSs are presented, differentiating among issues related to the efficiency and the effectiveness of the system. Among them, the important role played by performance analyses of the warning model for increasing the system efficiency is thoroughly discussed. [ABSTRACT FROM AUTHOR]

Model calibration is usually based on trial-and-error procedures that, in turn, rely on expert judgment or previously acquired experiences for similar phenomena. Efficient and reliable procedures for model calibration of the propagation stage of landslides are still needed. This paper addresses this issue by proposing an inverse analysis procedure and applying it to the case history of a short run-out landslide triggered by a rising perched water table after a heavy rainfall. It focuses on the key role played by the field observations used to set up the inverse analysis, and evaluating the reliability of the numerical simulations. It also investigates the effect of different types of optimization parameters on the inverse analysis results, referring to a mixed-phase model or to a two-phase model for the propagating soil. Several sets of observations are used; all of them refer to the soil deposit thickness at the end of propagation, but differ in both location and number of the adopted values. The numerical analysis of the case history is performed through the academic “GeoFlow_SPH” model, and model calibration by inverse analysis is conducted using the “UCODE” software. The results obtained are discussed with the aim to provide practical criteria to identify the minimum amount of information required for a satisfactory model calibration. [ABSTRACT FROM AUTHOR]

A process chain for the definition and the performance assessment of an operational regional warning model for rainfall-induced landslides, based on rainfall thresholds, is proposed and tested in a landslide-prone area in the Campania region, southern Italy. A database of 96 shallow landslides triggered by rainfall in the period 2003-2010 and rainfall data gathered from 58 rain gauges are used. First, a set of rainfall threshold equations are defined applying a well-known frequentist method to all the reconstructed rainfall conditions responsible for the documented landslides in the area of analysis. Several thresholds at different exceedance probabilities (percentiles) are evaluated, and nine different percentile combinations are selected for the activation of three warning levels. Subsequently, for each combination, the issuing of warning levels is computed by comparing, over time, the measured rainfall with the pre-defined warning level thresholds. Finally, the optimal percentile combination to be employed in the regional early warning system, i.e. the one providing the best model performance in terms of success and error indicators, is selected employing the 'event, duration matrix, performance' (EDuMaP) method. [ABSTRACT FROM AUTHOR]

Statistical analyses have been often used for landslide susceptibility zoning at small to medium scale when relevant base and thematic maps are available. Since the beginning of the last decade, images remotely acquired by spaceborne Synthetic Aperture Radar (SAR) and processed via Differential SAR Interferometry (DInSAR) proved extremely useful for non-invasive and non-destructive monitoring of displacements of the topographic surface. The present paper proposes an original procedure for the definition of the state of activity of slow-moving landslides via the combined use of multivariate statistical analyses and DInSAR data. The procedure is based on the following essential elements: distinction between terrain units used for computational purposes and the final zoning units; independent statistical and DInSAR analyses and activity models leading to first-level state of activity zoning maps; a consistency model between statistical and DInSAR analyses; two confidence and combination models leading, respectively, to second- or third-level state of activity zoning maps. The application in a test area including 19 municipalities in southern Italy, where slow-moving landslides are widespread and accurately mapped by using geomorphological criteria, allowed the generation of the three above-mentioned levels of zoning maps. The results were successfully crosschecked by exploiting a different DInSAR dataset and the results of previous works based on the use of slow-moving landslide-induced damage to facilities surveys. [ABSTRACT FROM AUTHOR]

Highlights from the article: The International School on "LAndslide Risk Assessment and Mitigation" (LARAM) (http://www.laram.unisa.it) was founded at the University of Salerno in 2005 by Prof. Leonardo Cascini, who served as the Director of the School from 2005 to 2017, within the geotechnical engineering research group of the Department of Civil Engineering. Since 2006, 11 editions were held in Salerno, Italy, in the month of September and three editions were held in Chengdu, China. Graph: Fig. 2Pictures from the last edition of the LARAM doctoral school, held at the University of Salerno in September 2018.

The paper proposes a methodology, in two successive steps, to zone the susceptibility to shallow landslides in fine grained soils by means of statistical methods. The first step of the methodology, aiming at defining, calibrating and validating the statistical analysis, ends with a landslide susceptibility computational map; the second step of the methodology is employed to produce a susceptibility map for zoning purposes. This structured methodology arises from the need to distinguish, at any given scale of analysis, between the spatial discretization needed to perform the statistical computations (terrain computational units) and terrain units useful for zoning purposes (terrain zoning units). The applicability of the proposed methodology is tested, at two different scales (1:25,000 and 1:5000), in two areas of southern Italy, the test area for the larger scale being a portion of the test area used for the analysis at the smaller scale. This allows for the generalization of the obtained results through the comparison, for the same phenomena in the same geo-environmental context, of the predisposing factors at two scales of analysis. In both analyses, the relevant variables for the susceptibility assessment are: elevation zone, slope gradient, slope curvature and geology; in the analysis at large scale also the weathered rock thickness, available only at this scale, assumes a relevant role. In both cases, the aggregation of multiple terrain computational units (TCU) into a larger terrain zoning unit (TZU) works best when focal statistic techniques are used with a characteristic dimension of the area of influence equal to 16 TCUs. [ABSTRACT FROM AUTHOR]

Perceptions of risk are a key issue when seeking to develop systems, practices and policies to protect local populations. This is particularly evident when risk mitigation strategies involve non-structural measures such as relocation and warning systems which presuppose the active involvement of the communities in question. This study adopts an interdisciplinary approach to studying the perceptions, knowledge and opinions on landslide risk amongst residents in Sarno, a small town in Southern Italy which was hit by disastrous landslides on 5-6 May 1998. The paper presents the results of a survey conducted in the months of March, April and May 2013 using a purpose-designed questionnaire. The survey was conducted using face-to-face interviews with 100 residents, 60 of whom live inside the so-called 'red zone', a territory declared at high residual risk soon after the events of 1998. The questionnaire included questions relating to perceived risk exposure, trust in institutions responsible for risk management, evaluations of risk mitigation measures and the early-warning strategy. The results of the study clearly emerges, amongst other issues, that the organisms which are responsible for risk management in Sarno need to develop more effective communication strategies in order to transmit knowledge about the actions implemented to reduce landslide risk in the area. [ABSTRACT FROM AUTHOR]

The international school for PhD students and young doctors on “LAndslide Risk Assessment and Mitigation” (LARAM) is presented and, specifically, information is provided about the next edition of the doctoral school that will held in Italy, at the University of Salerno, on September 3-14, 2018. The LARAM School is managed by the Geotechnical Engineering Group (GEG) of the University of Salerno, which recently became an Associate member of the International Consortium on Landslides (ICL). The 2018 edition of the LARAM School is organised as an ICL/IPL activity. [ABSTRACT FROM AUTHOR]

A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot: a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies. [ABSTRACT FROM AUTHOR]

Abstract: A procedure is proposed to produce landslide distribution zoning maps to be considered preparatory to susceptibility, hazard and risk zoning maps, based on 1) the results from a statistical multivariate analysis of a landslide inventory, which must be available for only a portion of the territory to be zoned, and 2) the use of appropriately defined terrain mapping units. The units are divided into terrain computational units (TCUs) and terrain zoning units (TZUs), whose size is related to the scale of zoning. The procedure comprises three phases: calibration, validation and prediction. The purpose of the prediction phase is the application of a calibrated and validated statistical model in a territory, previously recognized as viable on the basis of ‘a-priori applicability maps,’ for which no information is available regarding the distribution of landslides or where the information provided by the landslides inventory is unreliable or heterogeneous. The proposed procedure is applied to two case studies in southern Italy for the analysis and zoning of slow-moving landslides at 1:25,000 and 1:100,000 scales, respectively. The first case study illustrates the applicability of the procedure. The aim of the second case study is to address the part of the procedure related to the evaluation of the computational maps at the end of the calibration and validation phases. [Copyright &y& Elsevier]

Active slow-moving landslides in clayey soils exhibit continuous movements generally controlled both in the accelerating and decelerating phases by the pore-water pressure regime that, in turn, is strictly correlated to the net rainfall regime. The paper stresses the importance of a reliable groundwater model to predict these types of movements. To this aim a procedure is proposed to define the transient groundwater regime in the slope on the basis of recorded rainfall and monitoring data; the model is then used to derive the time-dependent shear strength along the main slip surfaces. The displacements at selected points along the slip surface are computed using a phenomenological (i.e., empirical) relationship between the local factor of safety and the displacement rate at those points. The procedure is employed for the analysis of a well-documented case history: the Porta Cassia landslide (central Italy). [ABSTRACT FROM AUTHOR]

A set of four thematic articles dealing with "Community Participation for Landslide Disaster Risk Reduction" will be published in this issue of Landslides following a call for papers, promoted by the authors, aimed at gathering field experiences in implementing and adopting landslide risk reduction measures at the community level in different social and economic environments. This editorial article briefly introduces the topic, states the objectives and the main findings of the four research studies, and provides some concluding remarks. [ABSTRACT FROM AUTHOR]

Weather-induced shallow landslides in coarse-grained soils are first failure phenomena often occurring as multiple slides in natural slopes over wide areas. Diffuse monitoring over the area of interest is required if one wants to predict the possible location of future landslides or the soil conditions leading to triggering for early warning purposes. This study aims at highlighting the role played by ground-based monitoring devices when they are used, singularly or combined together, as tools to assess the conditions of slopes in granular soils leading to shallow first-failure slides. Monitoring is discussed in relation to expectations coming from analytical and numerical models within an "observational modelling" framework, i.e. methods and procedures that use inverse analysis techniques to update the numerical model of a boundary value problem using monitoring data. The influence of the location of the monitoring instruments is assessed in different stratigraphic conditions and considering the saturation degree of the soil layers. The time-dependent soil parameters considered are the following: positive pore water pressure, suction, soil water content, stress, surface and subsurface displacements. All these parameters can be monitored employing static permanently deployed point sensors and, potentially, using low-cost instruments providing high resolution data in time and space. The focus of the contribution is on the meaning of the measurements as potential detectors of soil conditions leading to failure within a slope. To this aim, a series of parametric analyses employing analytical and numerical methods, in 1D and 2D conditions, will be presented. The analyses investigate the soil response to applied boundary conditions. When only hydraulic modelling is performed, the prediction of the groundwater regime in a slope in relation to time-dependent atmospheric conditions is used as input in limit equilibrium slope stability analyses to derive the variation with time of the factors of safety along potential slip surfaces. When hydromechanical coupling is explicitly considered, the numerical simulations are aimed at reproducing the stress-strain evolution associated with the transient seepage processes induced by changing hydraulic boundary conditions. In both cases, the results of the parametric analyses are used to evaluate the modelled response of a slope at the onset of failure, in different locations and in relation to many monitoring parameters (e.g., pore water pressures, soil water contents, stresses, displacements). The presented results should be seen as a contribution towards the aim of defining and adopting an economically sustainable and technically reliable monitoring strategy for early warning purposes, by predicting the conditions leading to the triggering of shallow slides in coarse-grained soils over wide areas. [ABSTRACT FROM AUTHOR]

Landslide early warning systems (LEWS) represent a significant option for addressing weather-induced landslides worldwide. The structure of LEWS can be schematized as an interrelation of three main modules: landslide model, warning model, and warning system. Warning models developed for weather-induced landslides are mainly based on correlation laws, for which thresholds are defined considering one or more combinations of measured rainfall that have led (or not led) to slope movements. However, the distinction between triggering and non-triggering rainfall conditions is not trivial, thus conventional methods adopted to this aim are often highly subjective. Moreover, data on landslide occurrences and rainfall measurements are often either not available or accessible only to a restricted number of scientists. In this study information from an open-access non-conventional landslide inventory and rainfall data from satellite monitoring have been employed within a conceptual framework for the development of a probabilistic warning model. The main steps of the proposed approach are: collection of input data, correlation between landslides and rainfall combinations, and probabilistic analysis. Data on landslide occurrences are derived from the FraneItalia catalog (https://data.mendeley.com/datasets/zygb8jygrw/1), a landslide inventory based on information retrieved from online Italian news. The database currently spans from 2010 to 2017 and contains 8931 landslides, most of them triggered by rainfall. Rainfall measurements are derived from the satellite-based NASA Global Precipitation Measurement (GPM) database, which contains gridded precipitation and precipitation-error estimates, with a half-hour temporal resolution and a 0.10-degree spatial resolution, covering most of the earth starting from March 2014. Successively, an algorithm approach based on a reduced set of parameters has been applied to reconstruct the rainfall events, to identify the rainfall conditions that resulted in landslides and to measure the duration and the cumulated rainfall for the events. Finally, a Bayesian methodology has been developed to determine landslide conditional probabilities associated to each rainfall combination.The proposed procedure has been tested by analysing the reported landslides from March 2014 to December 2017 within the eight warning zones defined for hydrogeological risk management in the Campania region (Italy). Although this study is only in a preliminary phase, the results achieved herein demonstrates the potential of the proposed probabilistic method to define objective and reproducible rainfall thresholds for landslide early warning purposes. [ABSTRACT FROM AUTHOR]

Background: In Italy landslides are widespread natural phenomena causing a significant number of fatalities and huge economic losses throughout the country every year. Information on the spatial and temporal distribution of landslides at national scale is critical for developing landslide susceptibility, hazard and risk maps, as well as, more generally, for decision making in landslide risk management.Description: The paper presents, after a brief review on global and national landslide databases, a new geo-referenced catalog of recent landslides affecting the Italian territory. The catalog, called Franeitalia, includes both fatal landslide events and events that did not produce physical harm to people. It has been developed consulting online news sources from 2010 onwards. The following seven steps have been performed to define and populate the catalog: i) selection of news sources; ii) identification of effective search keywords; iii) collection of relevant news articles; iv) identification of landslide categories; v) definition of catalog fields; vi) information mining from news articles; vii) geo-referencing of the events. Landslide events are classified considering two numerosity categories and three consequence categories. The numerosity categories are: single landslide events (SLE), for records only reporting one landslide; and areal landslide events (ALE), for records referring to multiple landslides triggered by the same cause in the same geographic area. Both SLEs and ALEs are divided in three consequence classes according to whether the event produced victims and/or missing people (C1, very severe), injured persons and/or evacuations (C2, severe), or did not cause any physical harm to people (C3, minor). Information on the landslide events collected in the catalog always includes: data on the location of the event, day of occurrence of the landslide (s), source (s) of information, and number of landslides in case of areal events. Additional information may include: onset and duration of the landslide event, landslide characteristics, phase of activity, details on the consequences.Conclusions: Reports and statistics on the landslides included in the catalog are presented highlighting: the main figures of the landslide inventory, currently spanning from the 2010 to 2017 and including 8931 landslides; and time-dependent national and regional trends, with a focus on the consequences induced by the events. The paper also compares and discusses the figures in relation to other catalogs reporting recent landslides that occurred in the Italian territory. [ABSTRACT FROM AUTHOR]

Shallow landslides are widespread in different geological contexts and generally occur as multiple events over large areas. When these phenomena involve fine-grained soils, they may cause serious consequences—in terms of environmental and property damages—and thus their spatial forecasting becomes a relevant issue for land use planning and design purposes. The existing literature provides several methods for landslide susceptibility assessment, categorized in qualitative and quantitative methods. When dealing with analyses at large scale (1:5000), quantitative methods are generally preferred. In this paper, landslide susceptibility maps are produced for a study area prone to shallow landsliding, located in Catanzaro (southern Italy). To this aim, two quantitative methods are implemented: the statistical “information value method” and the deterministic “TRIGRS model.” The two approaches are compared by means of two indicators of the grade of correctness of the landslide susceptibility maps: the area under curve of the ROC curve, AUC, and the overestimation index, OI. The results of the analyses in terms of AUC values demonstrate the effectiveness and consistency of both methods in performing the susceptibility mapping of the study area. When the OI values are considered, the results provided by the deterministic model are slightly better than the ones resulting from the statistical analysis. This does not come as a surprise for the case study at hand and it can be ascribed to the availability, within the study area, of: a reliable database of soil properties, and an in-depth knowledge of the behaviour of the considered landslides. [ABSTRACT FROM AUTHOR]