Landslide Database Research Articles

Probabilistic evaluation of loess landslide impact using multivariate model

The Loess Plateau is the largest loess accumulation area around the world, onto which loess landslides have been occurring frequently each year, thus bringing significant threats to communities there. To mitigate or manage the risk brought by loess landslides, many methods have been developed to gain insights into mechanisms that trigger loess landslides, or to identify regions that are susceptible to landslides through landslide susceptibility mapping. However, none of these methods can be used to quantitatively evaluate possible impact of potentially unstable slopes, which offers important information for risk management, especially for regions with high susceptibility to landslides. This study aims to fill this gap by constructing a loess landslide database first from field investigation. Then, a multivariate model for loess landslide data, including its height, width, area, and length, is developed considering correlation among these parameters. Subsequently, the multivariate model is used to predict statistically and quantitatively impact of a potentially unstable loess slope, in terms of slide width, length, and area, given height of the potentially unstable loess slope. The proposed method is applied to loess landslides occurred in Baoji City for illustration. Results show that the proposed method works reasonably well. In addition, some key equations are provided using results from the multivariate model. With these equations, geotechnical engineers or decision-makers can evaluate possible impact of a potentially unstable loess slope with minimal effort.

A GIS tool for infinite slope stability analysis (GIS-TISSA)

Landslides are one of the most common and a destructive natural hazard in mountainous terrain and thus evaluating their potential locations and the conditions under which they may occur is crucial for their hazard assessment. Shallow landslide occurrence in soil and regolith covered slopes are often modeled using the infinite slope model, which characterizes the slope stability in terms of a factor of safety (FS) value. Different approaches have been followed to also assess and propagate uncertainty through such models. Haneberg (2004) introduced the use of the First Order Second Moment (FOSM) method to propagate input uncertainty through the infinite slope model, further developing the model and implementing it in the PISA-m software package (Haneberg, 2007). Here we present an ArcPy implementation of PISA-m algorithms, which can be run from ESRI ArcMap in an entirely consistent georeferenced framework, and which we call “GIS Tool for Infinite Slope Stability Analysis” (GIS-TISSA). Users can select between different input options, e.g., following a similar input style as for PISA-m, i.e., using an ASCII.csv parameters input file, or providing each input parameter as a raster or constant value, through the program graphic user interface. Analysis outputs can include FS mean and standard deviation estimates, the probability of failure (FS < 1), and a reliability index (RI) calculation for FS. Following the same seismic analysis approach as in PISA-m, the Newmark acceleration can also be done, for which raster files of the mean, standard deviation, probability of exceedance, and RI are also generated. Verification of the code is done by replicating the results obtained with the PISA-m code for different input options, within a 10−5 relative error. Monte Carlo modeling is also applied to validate GIS-TISSA outputs, showing a good overall correspondence. A case study was performed for Kannur district, Kerala, India, where an extensive landslide database for the year 2018 was available. 81.19% of the actual landslides fell in zones identified by the model as unstable. GIS-TISSA provides a user-friendly interface, particularly for those users familiar with ESRI ArcMap, that is fully embedded in a GIS framework and which can then be used for further analysis without having to change software platforms and do data conversions. The ArcPy toolbox is provided as a .pyt file as an appendix as well as hosted at the weblink: https://pages.mtu.edu/~toommen/GeoHazard.html.

Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

Landslides can cause severe problems to the social and economic well-being. In order to effectively mitigate landslide hazards, the development of detailed susceptibility maps is required, towards implementing targeted risk management plans. This study aims to create detailed landslide susceptibility (LS) and landslide risk (LR) maps of the Sperchios River basin by applying an expert semi-quantitative approach that integrates the Geographic Information Systems (GIS)-based multicriteria analysis and Earth Observation (EO) data. Adopting the analytic hierarchy process (AHP) for a weighted linear combination (WLC) approach, eleven evaluation parameters were selected. The results were validated using a historic landslide database, enriched with new landslide locations mapped by satellite and aerial imagery interpretation and field surveys. Moreover, the landslide risk map of the area was also developed, based on the LS delineation, considering additionally the anthropogenic exposure and overall vulnerability of the area. The results showed that the most susceptible areas are located at the west and south-west regions of the basin. The synergistic use of GIS-based analysis and EO data can provide a useful tool for the design of natural hazards prevention policy at highly susceptible to risk landslide risk areas.

A Regional-Scale Landslide Early Warning System Based on the Sequential Evaluation Method: Development and Performance Analysis

A regional-scale landslide early warning system was developed in collaboration with a city government. The structure and distinctive features of the system are described in detail. This system employs the principles of the sequential evaluation method that consecutively applies three different evaluation stages: statistical, physically based, and geomorphological evaluations. Based on this method, the system determines five phases of warning levels with improved levels of certainty and credibility. In particular, the warning levels are systematically derived to enable the discrimination of slope failures and debris flows. To provide intuitive and pragmatic information regarding the warning capabilities of the system, a comprehensive performance analysis was conducted. Early warning level maps were generated and a historical landslide database was established for the study period from 2009 to 2016. As a result, 81% of historical slope failures and 86% of historical debris flows were correctly predicted by high-class warning levels. Miscellaneous details associated to the timing efficiency of warnings were also investigated. Most notably, five high-class warning level events and four landslide events were recorded for a study region during the eight-year period. The four landslide events were all successfully captured by four out of the five warning events.

Probabilistic mapping of earthquake-induced submarine landslide susceptibility in the South-West Iberian margin

The SW Iberian continental margin is well recognized as a tectonically active area, where major canyons and mass wasting events are both present. Earthquake triggered submarine landslides may cause tsunami and result in catastrophic damage to bordering coastal areas. In this setting, submarine landslide susceptibility mapping represents a major step towards a regional risk mitigation strategy. Landslide susceptibility mapping in large offshore areas presents significant challenges as a result of the limited information on controlling variables, large uncertainties in triggering mechanisms and limited geotechnical data. In this study, a geotechnical model-based approach has been followed that narrows the range of controlling factors and, within a probabilistic framework, allows a systematic treatment of parameter uncertainties. This model-based analysis covers the whole SW Iberian margin increasing by three orders of magnitude the areal extent of precedent offshore slope stability susceptibility studies. This jump in spatial scale is facilitated by application of a systematic Bayesian updating procedure, to combine geotechnical information from global databases and that available from regional sites. Seismic shaking is estimated using an available regional database of seismogenic faults. These tools are implemented within a GIS to generate, via Montecarlo simulations, probabilistic landslide susceptibility maps based on two different analytical seismic infinite slope stability models. These models differ mainly in the form of their final results, either as distributions of slope stability safety factors or as distributions of seismic-triggered slope displacements. Receiving Operator Curves are used to assess the different landslide susceptibility predictions obtained against a comprehensive regional database of submarine landslides. It turns out that the models analyzed correctly predict 92% and 82% of the mapped landslide subset chosen for validation for pseudo-static and displacement-based method respectively. This suggests that, within the limits of the currently available databases, seismic events are the dominant factor at the origin of the submarine landslides mapped in the study area. An advantage of the framework presented is that it can quickly incorporate new regional geotechnical information or better regional landslide databases, as they become available.

Landslides in the Andes: Forests can provide cost-effective landslide regulation services

Landslides cause billions of dollars (USD) in damage and hundreds of life losses every year in mountainous areas globally, and these effects are exacerbated by climate change and increased human occupation of vulnerable areas. In many mountainous regions forests deliver slope stability, helping to prevent landslides. However, forests are progressively converted into other land uses in many mountainous regions. In this study, we focus on the Colombian Andes, the most populated and deadly landslide-prone part of Colombia. We aim to determine the difference in frequency of landslides from forested and non-forested areas, and subsequently, quantify the potential costs and benefits of protecting forest and of restoring forest from agricultural lands. To that end, we combine economic data with geographical information related to public and private infrastructure, land use, and landslide susceptibility. Analyzing the national landslide database of Colombia, we established that landslides are almost six times (581%) more likely to occur on non-forested lands than on forested lands. From an economic perspective, by preventing landslides, forests provide a net benefit through the provision of slope stability services. Our most conservative estimates indicate it is 16 times more cost-effective to promote forest corridors, via conservation or reforestation along roads by paying farmers and cattle herders their opportunity costs, than for the public to pay the expected value of landslide damage. Our analysis provides strong evidence that vegetated hillsides can provide a cost-effective ecosystem service approach to mitigate economic losses due to landslides in one of the world's most landslide prone areas.

Comparative Analysis of Pixel and Object Based Classification Approach for Rapid Landslide Delineation with the Aid of Open Source Tools in Garhwal Himalaya

Abstract Landslides are the natural geomorphic processes that are essential for the landscape development. Loss of tremendous amount of life, natural resources as well as property has made this phenomenon a natural disaster. Number of researches have been done to understand this phenomenon and how to overcome this calamity with the aid of upcoming modern information services and technological advancements. Despite all these, there are still certain undocumented slope failure events due to inaccessibility and lack of a proper database. With the advancement of satellite remote sensing and geographical information system (GIS), it has certainly become easier to monitor and prepare landslide database especially in rough and rugged terrain of the Himalaya for delineation of risk zones. Free availability of high-resolution images and an open source efficient software have certainly been proved advantageous for this purpose. This paper aims at quick and accurate landslide inventory mapping, using high resolution Sentinel 2 data along with normalized difference vegetation index (NDVI), over which unsupervised and object-based image analysis (OBIA) was done to extract landslide features in an efficient manner. Both these processes were achieved with the aid of open source SAGA (System for Automated Geoscientific Analyses) software. The performances of these classifications were analyzed for their quantitative reliability in extracting the landslide features in two different areas (test area 1 and 2). In this, the object based image classification outstands, with the overall accuracy percentage difference of 17.11% higher than pixel-based classification approach in test area 1 and about 21% higher in test area 2.

Estimation of rainfall thresholds for shallow landslides in the Sierra Madre Oriental, northeastern Mexico

Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico, causing significant damage to infrastructure. In this work, we have studied the connection between rainfall and landslides in the Santa Rosa Canyon, a catchment located in the northeastern Mexico. A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed. A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides. For each event the duration (D, in hours) and the cumulated rainfall event (E, in mm) were determined by using historical rainfall data from weather stations located near the study area. We have proposed an ED threshold for rainfall-induced landslides with durations 0.5 < D < 120 hours to address the conditions that trigger these events in the study area. On analyzing the obtained threshold, it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area. This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon. Finally, we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis. A good approach was obtained, especially for rainfall events with daily measurements. Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico, and replicated for other landslide prone areas in the region.

Assessing the potential of soil moisture measurements for regional landslide early warning

In mountainous terrain, rainfall-induced landslides pose a serious risk to people and infrastructure. Regional landslide early warning systems (LEWS) have proven to be a cost-efficient tool to inform the public about the imminent landslide danger. While most operational LEWS are based on rainfall exceedance thresholds only, recent studies have demonstrated an improvement of the forecast quality after the inclusion of soil hydrological information. In this study, the potential of in situ soil moisture measurements for regional landslide early warning is assessed. For the first time, a comprehensive soil moisture measurement database was compiled for Switzerland and compared with a national landslide database (Swiss flood and landslide damage database, WSL). The time series were homogenized and normalized to represent saturation values. From ensembles of sensors, the mean and standard deviation saturation were calculated and infiltration events were delimited, characterized, and classified as landslide-triggering or non-triggering based on the occurrence of landslides within a specified forecast distance. A logistic regression function was applied to model the landslide activity based on the infiltration event characteristics and several models were analysed and compared with receiver operating characteristics (ROC). A strong distance dependence becomes apparent showing a forecast goodness decrease with increasing distance between water content measurement site and landslide, and a better forecast goodness for long-lasting as opposed to short-duration precipitation events. While most variability can be explained by the two event properties antecedent saturation and change of saturation during an infiltration event, event properties that describe antecedent conditions are more important for long-lasting as opposed to short-duration precipitation events that can be better explained by properties describing event dynamics. Overall, the analysis demonstrated that in situ soil moisture data effectively contains specific information useful for landslide early warning.

Landslide identification using machine learning

Landslide identification is critical for risk assessment and mitigation. This paper proposes a novel machine-learning and deep-learning method to identify natural-terrain landslides using integrated geodatabases. First, landslide-related data are compiled, including topographic data, geological data and rainfall-related data. Then, three integrated geodatabases are established; namely, Recent Landslide Database (RecLD), Relict Landslide Database (RelLD) and Joint Landslide Database (JLD). After that, five machine learning and deep learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), boosting methods and convolutional neural network (CNN), are utilized and evaluated on each database. A case study in Lantau, Hong Kong, is conducted to demonstrate the application of the proposed method. From the results of the case study, CNN achieves an identification accuracy of 92.5% on RecLD, and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing. Boosting methods come second in terms of accuracy, followed by RF, LR and SVM. By using machine learning and deep learning techniques, the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem.

Hong Kong’s landslip warning system—40 years of progress

Early warning systems have often been considered an effective risk mitigation tools for landslides. In 1977, the Geotechnical Engineering Office (GEO) of Hong Kong government established the world’s first territorial-wide early warning system for landslide disaster. The Landslip Warning System (LWS) has then been continuously enhanced and upgraded in response to the enrichment of rainfall and landslide database, advancement in instrumentation techniques and change in public perception of landslide risk over the last 40 years. This article consolidates the extensive experience of Hong Kong in using the landslip early warning system (LEWS) as a landslide risk management tool. A comprehensive review on the development process of the rainfall-landslide prediction models is presented. The landslide prediction model evolved from a rainfall duration-intensity model (late 1970) to a simple rainfall threshold model (middle 1980 to late 1990), then to a rainfall-landslide density model (early 2000) and rainfall-landslide frequency model (middle 2000s onward). Through regular review and update of the prediction model taking into account the availability of more recent data and activities that alter landslide risks, the performance of prediction model could be enhanced. The number of rain gauges expanded from 20 to 92 to support the operation of different generations of LWS. The GEO is currently adopting internet of thing (IOT) and cloud computing technology to enhance the resilience of the LWS, especially during extreme weather condition.

Landslide databases for climate change detection and attribution

A systematic inventory of landslide events over regional spatial scales and through time is required for investigating changes in landslide frequency along-side changes in landslide triggers. This paper describes the methods used to compile a European-wide landslide inventory and some of the methodological and practical obstacles that inhibit better use and development of such inventories. We argue that these methods can be used more widely to provide a comprehensive picture of landslide populations and to further enrich our understanding of the impact of climate change and other drivers on landslide frequency and magnitude.

Analysing post-earthquake landslide susceptibility using multi-temporal landslide inventories — a case study in Miansi Town of China

The earthquake that occurred on May 12, 2008, in Wenchuan County aroused a great deal of research on co-seismic landslide susceptibility assessment, but there is still a lack of an evaluation method that considers the activity state of the landslide itself. Therefore, this paper establishes a new susceptibility evaluation model that superimposes the active landslide state based on previous susceptibility evaluation models. Based on a multi-phase landslide database, the probabilistic approach was used to evaluate landslide susceptibility in the Miansi town over many years. We chose the elevation, slope, aspect, and distance from the channel as trigger factors and then used the probability comprehensive discrimination method to calculate the probability of landslide occurrence. Then, the susceptibility results of each period were calculated by superposition with the activity rate. The results show that between 2008 and 2014, the proportion of areas with low landslide susceptibility in the study area was the largest, and the proportion of areas with the highest susceptibility was minimal. The landslide area with highest susceptibility gradually decreased from 2014 to 2017. However, in 2017, 15.06% of the area was still with high susceptibility, and relevant disaster prevention and reduction measures should be taken in these areas. The larger area under the receiver operating characteristic curve (AUC) indicates that the results of the landslide susceptibility assessment in this study are more objective and reliable than those of previous models. The difference in the AUC values over many years shows that the accuracy of the evaluation results of this model is not constant, and a greater number of landslides or higher landslide activity corresponds to a higher accuracy of the evaluation results.

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous Colombian Andes.

Landslides are a natural hazard that presents a major threat to human life and infrastructure. Although they are a very common phenomenon in Colombia, there is a lack of analysis that entails national and comprehensive spatial, temporal, and socioeconomic evaluations of such events based on historical records. This study provides a detailed assessment of the spatial and temporal patterns and the socioeconomic impacts associated with landslides that occurred in the country between 1900 and 2018. Two national landslide databases were consulted and this information was complemented by local and regional landslide catalogues. A total of 30,730 landslides were recorded in the 118-year period. Rainfall is the most common trigger of landslides, responsible for 92 per cent of those registered, but most fatalities (68 per cent) are due to landslides caused by volcanic activity and earthquakes. An 'fN curve' revealed a very high frequency of small and moderate fatal landslides in the time frame.

About this title - Subaqueous Mass Movements and their Consequences: Advances in Process Understanding, Monitoring and Hazard Assessments

This volume focuses on underwater or subaqueous landslides with the overarching goal of understanding how they affect society and the environment. The new research presented here is the result of significant advances made over recent years in directly monitoring submarine landslides, in standardizing global datasets for quantitative analysis, constructing a global database and from leading international research projects. Subaqueous Mass Movements demonstrates the breadth of investigation taking place into subaqueous landslides and shows that, while events like the recent ones in the Indonesian archipelago can be devastating, they are at the smaller end of what the Earth has experienced in the past. Understanding the spectrum of subaqueous landslide processes, and therefore the potential societal impact, requires research across all spatial and temporal scales. This volume delivers a compilation of state-of-the-art papers covering topics from regional landslide databases to advanced techniques for in situ measurements, to numerical modelling of processes and hazards.

Tectonic and geomorphic controls on the distribution of submarine landslides across active and passive margins, eastern New Zealand

Abstract Submarine landslides occur on continental margins globally and can have devastating consequences for marine habitats, offshore infrastructure and coastal communities due to potential tsunamigenesis. Therefore, understanding landslide magnitude and distribution is central to marine and coastal hazard planning. We present the first submarine landslide database for the eastern margin of New Zealand comprising &gt;2200 landslides occurring in water depths from c. 300–4000 m. Landslides are more prevalent and, on average, larger on the active margin compared with the passive margin. We attribute higher concentrations of landslides on the active margin to tectonic processes including uplift and oversteepening, faulting and seamount subduction. Submarine landslide scars are concentrated around canyon systems and close to canyon thalwegs. This suggests that not only does mass wasting play a major role in canyon evolution, but also that canyon-forming processes may provide preconditioning factors for slope failure. Results of this study offer unique insights into the spatial distribution, magnitude and morphology of submarine landslides across different geological settings, providing a better understanding of the causative factors for mass wasting in New Zealand and around the world.

Subaqueous mass movements in the context of observations of contemporary slope failure

Abstract The consequences of subaqueous landslides have been at the forefront of societal conscience more than ever in the last few years, with devastating and fatal events in the Indonesian Archipelago making global news. The new research presented in this volume demonstrates the breadth of ongoing investigation into subaqueous landslides, and shows that while events like the recent ones can be devastating, they are smaller in scale than those Earth has experienced in the past. Understanding the spectrum of subaqueous landslide processes, and therefore the potential societal impact, requires research across all spatial and temporal scales. This volume delivers a compilation of state-of-the-art papers covering regional landslide databases, advanced techniques for in situ measurements, numerical modelling of processes and hazards.

МАГНІТНІ ДОСЛІДЖЕННЯ ПРИРОДНИХ І ТЕХНОГЕННИХ ПРОЦЕСІВ ОБ'ЄКТІВ КРИТИЧНОЇ ІНФРАСТРУКТУРИ НА ПРИКЛАДІ ДІЛЯНКИ "ГЛИНКА"

The results of magnetic soil and underlying rocks studies at the area near Lake Glinka are considered. This study was performed as a part of the landslide investigation of the urban environment of the Kyiv agglomeration. The aim was to detect the natural and man-made processes affecting critical infrastructure. Under field condition we measured the volume magnetic susceptibility of soil κ (10-3 SI), and sampling was performed. Under laboratory conditions we measured and then calculated the mass-specific magnetic susceptibility χ (10-8 m3/kg), and its frequency dependence χFD (%). The soil of the study area is gray forest (Greyic Phaeozems Albic in WRB) with signs of urban soil. Magnetic studies were conducted at two points on the high landslide bank of the Lake Glinka, as well as on the opposite low bank. On the low bank, we organised measurements and sampling along the micro-catena and at the vertical soil section to study the soil genetic horizons. Magnetic polygons were constructed based on cosmography downloaded by ArcGIS. The classification of the polygons is made by the attracting the magnetic susceptibility and its frequency dependence. We identified the polygon with the highest values of magnetic susceptibility (χ = 54 × 10-8 m3/kg) and the lowest value of frequency dependence (χFD = 3.6 %). This is the soil with man-made pollution. Two polygons with similar values of magnetic parameters were identified: χ = 25-35 × 10-8 m3/kg, χFD = 8-10 %. Such values are related to the natural soils. Three polygons (two on the the low bank and one on the high landslide bank of Glinka) have average intensity values of magnetic parameters: χ=35-40 × 10-8 m3/kg, χFD = 6-7 %. The vertical distribution of soil magnetic parameters in genetic horizons was observed and the redistribution of magnetic matherial was detected. Such kind of the redistribution indicates the erosion processes. The magnetic properties of soils are important for the joint interpretation with electric tomography, GPR measurements, and GIS analysis of the national landslide database of Ukraine. The results are useful for the development of the algorithm for the environmental monitoring of the critical infrastructure at the urban area.

The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift

Predicting landslide occurrence is of key importance for understanding the geomorphological development of mountain environments as well as to assess the potential risk posed by landsliding to human societies in such environments. Global landslide susceptibility models use a generic model formulation to predict landslide susceptibility anywhere on the planet from openly available data. Regional models, on the contrary, use local information on landslide occurrence to constrain model parameters and may also benefit from better spatial information with respect to controlling factors. This study aims to investigate the added value of the construction of regional landslide susceptibility models (versus global and continental models) in the western branch of the East African Rift, a data-scarce landslide-prone tropical environment. First, a comprehensive landslide database containing 6446 instances was compiled for the study area using Google Earth imagery. Second, three regional data-driven landslide susceptibility models were developed. Third, the efforts to construct these regional models were quantified by analysing how their quality is impacted by (1) the use of more accurate, regional peak ground acceleration and geology data, and (2) an increasing inventory size. Fourth, regional and global/continental models were compared in terms of predictive power and geomorphological plausibility. We observe that global/continental landslide susceptibility models are capable of identifying landslide-prone areas, but lack prediction power and geomorphological plausibility when compared to our regional models. Importantly, this difference in quality is not driven by the use of more accurate and detailed geology and peak ground acceleration data, but rather by the use of a detailed regional landslide inventory to calibrate the models. We also show that the model quality only increases marginally beyond a certain inventory size. We conclude that the regional landslide susceptibility assessment does provide an added value compared to existing global models in terms of geomorphological plausibility and model performance, whereby the largest gain is to be found in the construction of a regional landslide inventory, rather than the investment in more detailed covariates or the application of more complex modelling techniques. The latter suggests that the role of controlling variables depends, to some extent, on the regional context: making adequate susceptibility predictions proves difficult when local conditions are not accounted for by means of a regional inventory.