Estimating the effects of water-induced shallow landslides on soil erosion

Rainfall induced landslides and soil erosion are part of a complex system of multiple interacting processes, and both are capable of significantly affecting sediment budgets. These sediment mass movements also have the potential to significantly impact on a broad network of ecosystems health, functionality and the services they provide. To support the integrated assessment of these processes it is necessary to develop reliable modelling architectures. This paper proposes a semi-quantitative integrated methodology for a robust assessment of soil erosion rates in data poor regions affected by landslide activity. It combines heuristic, empirical and probabilistic approaches. This proposed methodology is based on the geospatial semantic array programming paradigm and has been implemented on a catchment scale methodology using Geographic Information Systems (GIS) spatial analysis tools and GNU Octave. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. In order to improve computational reproducibility, the geospatial data transformations implemented in ESRI ArcGis are made available in the free software GRASS GIS. The proposed modelling architecture is flexible enough for future transdisciplinary scenario analysis to be more easily designed. In particular, the architecture might contribute as a novel component to simplify future integrated analyses of the potential impact of wildfires or vegetation types and distributions, on sediment transport from water induced landslides and erosion.Comment: 14 pages, 4 figures, 1 table, published in IEEE Earthzine 2014 Vol. 7 Issue 2, 910137+ 2nd quarter theme. Geospatial Semantic Array Programming. Available: http://www.earthzine.org/?p=91013

Semantic array programming in data-poor environments: assessing the interactions of shallow landslides and soil erosion

This research was conducted with the main objective to better integrate and quantify the role of water-induced shallow landslides within soil erosion processes, with a particular focus on data-poor conditions. To fulfil the objectives, catchment-scale studies on soil erosion by water and shallow landslides were conducted. A semi-quantitative method that combines heuristic, deterministic and probabilistic approaches is here proposed for a robust catchment-scale assessment of landslide susceptibility when available data are scarce. A set of different susceptibility-zonation maps was aggregated exploiting a modelling ensemble. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques such as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN), and two different landslide-susceptibility techniques based on the infinite slope stability model. The good performance of the ensemble model, when compared with the single techniques, make this method suitable to be applied in data-poor areas where the lack of proper calibration and validation data can affect the application of physically based or conceptual models. A new modelling architecture to support the integrated assessment of soil erosion, by incorporating rainfall induced shallow landslides processes in data-poor conditions, was developed and tested in the study area. This proposed methodology is based on the geospatial semantic array programming paradigm. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. By analysing modelling results within the study catchment, each year, on average, mass movements are responsible for a mean increase in the total soil erosion rate between 22 and 26% over the pre-failure estimate. The post-failure soil erosion rate in areas where landslides occurred is, on average, around 3.5 times the pre-failure value. These results confirm the importance to integrate landslide contribution into soil erosion modelling. Because the estimation of the changes in soil erosion from landslide activity is largely dependent on the quality of available datasets, this methodology broadens the possibility of a quantitative assessment of these effects in data-poor regions

Landslide and debris flow warning at regional scale. A real-time system using susceptibility mapping, radar rainfall and hydrometeorological thresholds

Rainfall triggered shallow slides and debris flows constitute a significant hazard that causes substantial economic losses and fatalities worldwide. Regional-scale risk mitigation for these processes is challenging. Therefore, landslide early warning systems (LEWS) are a helpful tool to depict the time and location of possible landslide events so that the hazardous situation can be managed more effectively. The main objective of this thesis is to set up a regional-scale LEWS that works in real-time over Catalonia (NE Spain). The developed warning system combines in real-time susceptibility information and rainfall observations to issue qualitative warnings over the region. Susceptibility has been derived combining slope angle and land use and land cover information with a simple fuzzy logic approach. The LEWS input rainfall information consists of high-resolution radar quantitative precipitation estimates (QPEs). To assess if a rainfall situation has the potential to trigger landslides, the LEWS applies a set of intensity duration thresholds. Finally, a warning matrix combines susceptibility and rainfall hazard to obtain a qualitative warning map that classifies the terrain into four warning classes. The evaluation of the LEWS performance has been challenging because of the lack of a systematic inventory, including the time and location of recent landslides events. Within the context of this thesis, a citizen-science initiative has been set up to gather landslide data from reports in social networks. However, some of the reports have significant spatial and temporal uncertainties. With the aim of finding the most suitable mapping unit for real-time warning purposes, the LEWS has been set-up to work using susceptibility maps based on grid-cells of different resolutions and subbasins. 30 m grid-cells have been chosen to compute the warnings as they offer a compromise between performance, interpretability of the results and computational costs. However, from an end users’ perspective visualising 30 m resolution warnings at a regional scale might be difficult. Therefore, subbasins have been proposed as a good option to summarise the warning outputs. A fuzzy verification method has been applied to evaluate the LEWS performance. Generally, the LEWS has been able to issue warnings in the areas where landslides were reported. The results of the fuzzy verification suggest that the LEWS effective resolution is around 1 km. The initial version of the LEWS has been improved by including soil moisture information in the characterisation of the rainfall situation. The outputs of this new approach have been compared with the outputs of LEWS using intensity-duration thresholds. With the new rainfall-soil moisture hydrometeorological thresholds, fewer false alarms were issued in high susceptibility areas where landslides had been observed. Therefore, hydrometeorological thresholds may be useful to improve the LEWS performance. This study provided a significant contribution to regional-scale landslide emergency management and risk mitigation in Catalonia. In addition, the modularity of the proposed LEWS makes it easy to apply in other regions.Els lliscaments superficials i els corrents d’arrossegalls són un fenomen perillós que causa significants perdudes econòmiques i humanes arreu del món. La seva principal causa desencadenant és la pluja. La mitigació del risc degut a aquets processos a escala regional no es senzilla. Ena quest context, els sistemes d’alerta són una eina útil per tal de predir el lloc i el moment en que es poden desencadenar possibles esllavissades en el futur, i poder fer una gestió del risc més eficient. L’objectiu principal d’aquesta tesi és el desenvolupament d’un sistema d’alerta per esllavissades a escala regional, que treballi en temps real a Catalunya. El Sistema d’alerta que s’ha desenvolupat combina informació sobre la susceptibilitat del terreny i estimacions de la pluja d’alta resolució per donar unes alertes qualitatives arreu del territori. La susceptibilitat s’ha obtingut a partir de la combinació d’informació del pendent del terreny, i els usos i les cobertes del sòl utilitzant un mètode de lògica difusa. Les dades de pluja són observacions del radar meteorològic. Per tal d’analitzar si un determinat episodi de pluja te el potencial per desencadenar esllavissades, el sistema d’alerta utilitza un joc de llindars intensitat-durada. Posteriorment, una matriu d’alertes combina la susceptibilitat i la magnitud del episodi de pluja. El resultat, és un mapa d’alertes que classifica el terreny en quatre nivells d’alerta. Amb l’objectiu de definir quina unitat del terreny és la més adient pel càlcul de les alertes en temps real, el sistema d’alerta s’ha configurat per treballar utilitzant mapes de susceptibilitat basats en píxels de diverses resolucions, i en subconques. Finalment, l’opció més convenient és utilitzar píxels de 30 m, ja que ofereixen un compromís entre el funcionament, la facilitat d’interpretació dels resultats i el cost computacional. Tot i això, la visualització de les alertes a escala regional emprant píxels de 30 m pot ser difícil. Per això s’ha proposat utilitzar subconques per oferir un sumari de les alertes. Degut a la manca d’un inventari d’esllavissades sistemàtic, que contingui informació sobre el lloc i el moment en que les esllavissades es van desencadenar, l’avaluació del funcionament del sistema d’alerta ha sigut un repte. En el context d’aquesta tesi, s’ha creat una iniciativa per tal de recol·lectar dades d’esllavissades a partir de posts en xarxes socials. Malauradament, algunes d’aquestes dades estan afectades per incerteses espacials i temporals força importants. Per a l’avaluació el funcionament del sistema d’alerta, s’ha aplicat un mètode de verificació difusa. Generalment, els sistema d’alerta ha estat capaç de generar alertes a les zones on s’havien reportat esllavissades. Els resultats de la verificació difusa suggereixen que la resolució efectiva del sistema d’alerta età al voltant d’1 km. Finalment, la versió inicial del sistema d’alerta s’ha millorat per tal poder incloure informació sobre l’estat d’humitat del terreny en la caracterització de la magnitud del episodi de pluja. Els resultats del sistema d’alerta utilitzant aquest nou enfoc s’han comparat amb els resultats que s’obtenen al córrer el sistema d’alerta utilitzant els llindars intensitat-durada. Mitjançant els nous llindars hidrometeorològics, el sistema emet menys falses alarmes als llocs on s’han desencadenat esllavissades. Per tant, utilitzar llindars hidrometeorològics podria ser útil per millorar el funcionament del sistema d’alerta dissenyat. L’estudi dut a terme en aquesta tesi suposa una important contribució que pot ajudar en la gestió de les emergències degudes a esllavissades a escala regional a Catalunya. A més a més, el fet de que el sistema sigui modular permet la seva fàcil aplicació en d’altres regions en un futur.Enginyeria del terren

Spatial prediction of landslide susceptibility/intensity through advanced statistical approaches implementation: applications to the Cinque Terre (Eastern Liguria, Italy)

Landslides are frequently responsible for considerable huge economic losses and casualties in mountainous regions especially nowadays as development expands into unstable hillslope areas under the pressures of increasing population size and urbanization (Di Martire et al. 2012). People are not the only vulnerable targets of landslides. Indeed, mass movements can easily lay waste to everything in their path, threatening human properties, infrastructures and natural environments. Italy is severely affected by landslide phenomena and it is one of the most European countries affected by this kind of phenomena. In this framework, Italy is particularly concerned with forecasting landslide effects (Calcaterra et al. 2003b), in compliance with the National Law n. 267/98, enforced after the devastating landslide event of Sarno (Campania, Southern Italy). According to the latest Superior Institute for the Environmental Protection and Research (ISPRA, 2018) report on "hydrogeological instability" of 2018, it emerges that the population exposed to landslides risk is more than 5 million and in particular almost half-million falls into very high hazard zones. The slope stability can be compromised by both natural and human-caused changes in the environment. The main reasons can be summarised into heavy rainfalls, earthquakes, rapid snow-melts, slope cut due to erosions, and variation in groundwater levels for the natural cases whilst slopes steepening through construction, quarrying, building of houses, and farming along the foot of mountainous zone correspond to the human component. This Ph.D. thesis was carried out in the Liguria region, inside the Cinque Terre National Park. This area was chosen due to its abundance of different types of landslides and its geological, geomorphological and urban characteristics. The Cinque Terre area can be considered as one of the most representative examples of human-modified landscape. Starting from the early centuries of the Middle Ages, local farmers have almost completely modified the original slope topography through the construction of dry-stone walls, creating an outstanding terraced coastal landscape (Terranova 1984, 1989; Terranova et al. 2006; Brandolini 2017). This territory is extremely dynamic since it is characterized by a complex geological and geomorphological setting, where many surficial geomorphic processes coexist, along with peculiar weather conditions (Cevasco et al. 2015). For this reason, part of this research focused on analyzing the disaster that hit the Cinque Terre on October, 25th, 2011. Multiple landslides took place in this occasion, triggering almost simultaneously hundreds of shallow landslides in the time-lapse of 5-6 hours, causing 13 victims, and severe structural and economic damage (Cevasco et al. 2012; D\u2019Amato Avanzi et al. 2013). Moreover, this artificial landscape experienced important land-use changes over the last century (Cevasco et al. 2014; Brandolini 2017), mostly related to the abandonment of agricultural activity. It is known that terraced landscapes, when no longer properly maintained, become more prone to erosion processes and mass movements (Lesschen et al. 2008; Brandolini et al. 2018a; Moreno-de-las-Heras et al. 2019; Seeger et al. 2019). Within the context of slope instability, the international community has been focusing for the last decade on recognising the landslide susceptibility/hazard of a given area of interest. Landslide susceptibility predicts "where" landslides are likely to occur, whereas, landslide hazard evaluates future spatial and temporal mass movement occurrence (Guzzetti et al., 1999). Although both definitions are incorrectly used as interchangeable. Such a recognition phase becomes crucial for land use planning activities aimed at the protection of people and infrastructures. In fact, only with proper risk assessment governments, regional institutions, and municipalities can prepare the appropriate countermeasures at different scales. Thus, landslide susceptibility is the keystone of a long chain of procedures that are actively implemented to manage landslide risk at all levels, especially in vulnerable areas such as Liguria. The methods implemented in this dissertation have the overall objective of evaluating advanced algorithms for modeling landslide susceptibility. The thesis has been structured in six chapters. The first chapter introduces and motivates the work conducted in the three years of the project by including information about the research objectives. The second chapter gives the basic concepts related to landslides, definition, classification and causes, landslide inventory, along with the derived products: susceptibility, hazard and risk zoning, with particular attention to the evaluation of landslide susceptibility. The objective of the third chapter is to define the different methodologies, algorithms and procedures applied during the research activity. The fourth chapter deals with the geographical, geological and geomorphological features of the study area. The fifth chapter provides information about the results of the applied methodologies to the study area: Machine Learning algorithms, runout method and Bayesian approach. Furthermore, critical discussions on the outcomes obtained are also described. The sixth chapter deals with the discussions and the conclusions of this research, critically analysing the role of such work in the general panorama of the scientific community and illustrating the possible future perspectives

Application of a fuzzy verification framework for the evaluation of a regional-scale landslide early warning system during the January 2020 Gloria storm in Catalonia (NE Spain)

The Gloria storm rainfalls affected Catalonia from 20 to 23 January 2020 and triggered multiple landslides, some of which affected buildings and infrastructures (such as roads and railways). This paper presents the rainfall and landslide datasets collected during the event, and evaluates the performance of a regional landslide early warning system (LEWS) during the Gloria storm applying a fuzzy verification method. The majority of the inventoried landslides can be classified as slides, involving a limited volume of sediment (up to 10 m3), and were triggered in cut slopes along linear infrastructures. Rainfall accumulations were significant in the whole region, especially in the Montseny area, where over 450 mm were registered in 96 h. Generally, the LEWS computed moderate and high warnings in the areas where large rainfall amounts were recorded, and showed good correspondence with the locations where landslides were reported. The fuzzy verification method has been applied using neighbouring windows of different sizes to obtain scale-dependant information on the LEWS performance. The skill of the LEWS considerably improves when enlarging the neighbouring window size from 500 m to 1 km.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The first author is supported by a grant from the Secretariat of Universities and Research of the Ministry of Business and Knowledge of the Generalitat de Catalunya. This work has been partly funded by the EC H2020 project ANYWHERE (DRS-01–2015–700099) and the Spanish national projects SMuCPhy and EROSLOP (BIA 2015–67500-R and PID2019–104266RB–I00/AEI/).Peer ReviewedPostprint (published version

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Recommendations for the quantitative analysis of landslide risk

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.JRC.H.5-Land Resources Managemen

A simplified semi-quantitative procedure based on the SLIP model for landslide risk assessment: the case study of Gioiosa Marea (Sicily, Italy)

Landslide risk assessment is fundamental in identifying risk areas, where mitigation measures must be introduced. Most of the existing methods are based on susceptibility assessment strongly site-specific and require information often unavailable for damage quantification. This study proposes a simplified methodology, specific for rainfall-induced shallow landslides, that tries to overcome both these limitations. Susceptibility assessed from a physically-based model SLIP (shallow landslides instability prediction) is combined with distance derived indices representing the interference probability with elements at risk in the anthropized environment. The methodology is applied to Gioiosa Marea municipality (Sicily, south Italy), where shallow landslides are often triggered by rainfall causing relevant social and economic damage because of their interference with roads. SLIP parameters are first calibrated to predict the spatial and temporal occurrence of past surveyed phenomena. Susceptibility is then assessed in the whole municipality and validated by comparison with areas affected by slide movements according to the regional databases of historical landslides. It is shown that all the detected areas are covered by points where the SLIP safety factor ranges between 0 and 2. Risk is finally assessed after computation of distances from elements at risk, selected from the land use map. In this case, results are not well validated because of lack of details in the available regional hydrogeological plan, both in terms of extension and information. Further validation of the proposed interference indices is required, e.g., with studies of landslide propagation, which can also allow considerations on the provoked damage

Predicting the Spatial Distribution of Rain-Induced Shallow Landslides by applying GIS and Geocomputational Techniques: A Case Study from North East India

This study presents a case of statistical modelling, by applying GIS and geocomputational techniques, to predict areas that are susceptible to future rain-induced shallow landslides. The statistical prediction model is based on the observed relationships between the spatial distribution of past landslideevents and environmental (causal) factors that are associated with such phenomena. The study also evaluates the predictive performance of a nonlinear regression model, namely the Generalized Additive Model(GAM),applied for the analysis. The study area comprises a residual hill of ? 6 Km2 area situated in the heart of Guwahati (capital city of Assam in NE India). We exploited the geoprocessing functions of SAGA GIS to derive nine different terrain attributesfrom a digital elevation model (DEM) processed by synthetic aperture radar interferometry (InSAR). The terrain attributes along with land use classes, in raster grid format, constitute the predictor variables. An inventory of the locations of eighty-two past occurrences of shallow landslide events constitutes the response. We performed the modelling and statistical geocomputation entirely in the open-source R language and software environment. The procedure comprises the following three steps: (1) Collinearityanalysis to discard redundant predictors. (2) 100-fold bootstrap resampling to fit the GAM by a random selection of 2/3 of the landslide pixels ("training" subset) and validate the GAM by the remaining 1/3 ("test" subset). (3) Estimate model accuracy (true error rates) by a repeated 100-fold 'hold-out validation' method and evaluate the predictive performance of the model by the Area under the ROC curve (AUROC) computed for 100 independently trained models. The mean and standard deviation of accuracy on training sets are 0.80 and 0.01, and that on test sets are 0.79 and 0.02 respectively. The AUROC corresponding to the meanof landslide probabilities is 0.87, and that of the 95% Confidence Intervals (CI) is between 0.86 and 0.88. Thevalues of these quality measures indicate that a data-driven model, such as the GAM, is efficient regarding its predictive performance, to highlight the unstable areas in the study area. We subsequently used the mean values of the landslide probability (susceptibility) estimates corresponding to each mapping unit (grid cell) to construct the landslide susceptibility map, which can be used for land use planning and hazard mitigation