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Statistical susceptibility assessment is a common approach applied worldwide for shallow landslide studies. Identification of morphological and geological conditions is essential and still incipient to evaluate the susceptibility of landslide events in the Brazilian territory. This study aimed to develop and compare shallow landslide susceptibility scenarios based on a bivariate statistical evaluation of geological (lithology and structures) and morphological (curvature, elevation, slope, and aspect) factors in Caraguatatuba, northern coast of Sao Paulo State in Brazil. A compilation of geological factors from published maps was made, and morphological maps were created based on Shuttle Radar Topography Mission (30 m). A bivariate statistical application by the informative value method was used to create four susceptibility scenarios, and the validation was achieved using the area under the curve (AUC). The results indicated that lithology was the more relevant conditioning factor, followed by elevation and slope. The methodology used to determine the susceptibility was efficient (AUC values between 0.809 and 0.841). The susceptibility scenario comparison identified that conditioning factors with the highest informational value generated the most accurate mapping. This indicates that using several conditioning factors does not necessarily generate a better map. This study contributes to shallow landslides research from a methodological perspective, as it is the first analysis of its kind in Serra do Mar Paulista, which are continuously affected by mass movements. Open-source data were chosen to be used, focusing on methodological applicability in other regions of the country, since resources for landslide studies in Brazil are low.

The Northwest of Portugal is a steep slope mountain area where high amounts of rainfall (3,000 mm/year) occur especially on winter, promoting high natural propensity to slope failure, mainly shallow landslides. This article aims to assess shallow landslide susceptibility, in Tibo catchment, Serra da Peneda, North of Portugal, using two physically based models—SHAllow Landslide STABility (SHALSTAB) and Safety Factor (SF)—applying a set of mechanical and hydrological parameters, assessed in situ and laboratory testing of soil samples collected in the field, calibrated by back analysis of landslides inventoried in the study area, as well as accurate topographic information derived from a high-resolution digital elevation model (DEM). The validation of results was made using shallow landslide scars, directly inventoried in the field. Both susceptibility model results were validated by scar concentration (SC) and landslide potential (LP). SHALSTAB model was also validated by minimum log q/T. SHALSTAB predicts 50 % of the area to be on unstable classes (log q/T

Landslides are a major natural hazard in the Bamenda highlands of Cameroon, and their occurrence in this region has most often been studied using qualitative methods. The aim of this research is to quantitatively assess the spatial probability of landslides using GIS and the informative value model. Landslide inventory was done through literature review, aerial photo-interpretation, participatory GIS and field survey. Six geo-environmental factors including slope, curvature, aspect, land use, lithology and geomorphology were used as landslide conditioning (static) factors. The susceptibility of the area to future landslide events was assessed by making a correlation between past landslides and geo-environmental factors using the informative value model. The landslide inventory involving 110 landslides was divided into two equal groups using random division criterion and was used to train and validate the model. The analysis showed that slope and land use are the most important causal factors of landslides in the area. The susceptibility index map predicted most landslides to occur around the steep slopes of the Bamenda escarpment that is being used for multiple anthropic activities. The training model had a success rate of 87%, and the validation model had a prediction rate of 90%. The prediction rate curve shows that 44, 32, 18 and 6% of future landslides will occur on 3, 8, 21 and 68% of the study area. The model correctly classified 89% of unstable areas and 81% of the stable areas with an accuracy rate of 0.90. This quantitative result complement other qualitative assessment results that show the Bamenda escarpment zone as a high-risk area. However, the area susceptible to landslide in this study goes beyond what earlier studies had indicated as houses and other infrastructure were found on old landslide sites whose scars have been eroded by human activities. This new input thus improves the quality of information placed at the disposal of civil protection units and land use managers during decision making.

The monsoon season in Cameroon is often marked by heavy rainfall and landslides across Bamenda Mountain Region causing heavy material damage and human casualties. This paper presents a model to assess the temporal probability of rainfall-induced landslides in the area. Landslide events from 1994 to 2013 collected from literature review, field work, aerial photo and satellite image interpretation, and rainfall data from the Bamenda weather station for the same period were used to determine rainfall thresholds for landslide initiation. Two threshold determination methods (intensity–duration and daily antecedent rainfall) were used to determine rainfall characteristics that initiate landslides. Then the number of times rainfall exceeded the different thresholds was estimated, and the results were fitted into the poison model to assess the temporal probability of landsliding for 1, 5, 10 and 25-year return period. Each of the thresholds was validated using rainfall and landslide events for 2009 that were not used in building the model. The results showed that rainfall intensity of 94 mm in 1 day and rainfall of 96 mm with 0.49 of 10-day antecedent rainfall triggered landslides in the area. The validation of these results showed only one landslide falling below each of the threshold curves. The temporal probability of landslides varied from 0.27 to 0.38 using the rainfall intensity–duration approach and 0.28–0.41 for the daily antecedent rainfall approach. The true forecast rate stood at 89 %, and the failure to predict probability was 0.11. The model shows a high correlation between rainfall and landsliding, and the temporal probability can be used for land-use planning and civil protection.

Floods and landslides are the natural hazards that most affect Northern Portugal. This paper presents the DISASTER database results for Northern Portugal, containing occurrences of floods and landslides between 1865 and 2010. The research carried out was based on press articles published in national and regional newspapers, but focusing exclusively the occurrences that caused personal damage, including deaths, injured, missing, evacuees and displaced people, regardless of the number of people affected and the economic value of damages. A total of 613 occurrences were recorded, which 18% are landslides and 82% are floods. These occurrences were responsible for 198 deaths, 58 injured, 34 disappeared, 1804 evacuated and 15,918 displaced. The trend analysis shows an increase of 0.17 occurrences/decade. The obtained results show the occurrences concentration in the more urbanized areas and along larger rivers.

This work presents and explores the Northern Portugal Landslide Database (NPLD) for the period 1900–2010. NPLD was compiled from press releases (regional and local newspapers) and technical reports (reports by civil protection authorities and academic works); it includes 628 landslides, corresponding to 5.7 landslides per year on average. Although 50% of landslides occurred in the last 35 years of the series, the temporal distribution of landslides does not show any regular increase with time. The relationship between annual precipitation and landslide occurrence shows that reported landslides tend to be more frequent in wetter years. Moreover, landslides occur mostly in the wettest months of the year (December, January and February), which reflects the importance of rainfall in triggering slope instability. Most landslides cause damage that affects people and/or structures; 69.4% of the landslides in Northern Portugal caused 136 fatalities, 173 injured and left 460 persons homeless. More than half of the total landslides (321 landslides) led to railway or motorway closures and 49 landslides destroyed 126 buildings. The NPLD is compared with a landslide database for the whole of Portugal constructed from a single daily national newspaper covering the same reference period. It will be demonstrated that the regional and local newspapers are more effective than the national newspaper in reporting damaging landslides in the North of Portugal. Like other documentary-based landslide inventories, the NPLD does not accurately report non-damaging landslides. Therefore, NPLD was found unsuitable to validate municipal-scale landslide susceptibility models derived from detailed geomorphology-based landslide inventories.

In the last century, Portugal was affected by several natural disasters of hydro-geomorphologic origin that often caused high levels of destruction. However, data on past events related to floods and landslides were scattered. The Disaster project aims to bridge the gap on the availability of a consistent and validated hydro-geomorphologic database for Portugal, by creating, disseminating and exploiting a GIS database on disastrous floods and landslides for the period 1865–2010, which is available in http://riskam.ul.pt/disaster/en . Data collection is steered by the concept of disaster used within the Disaster project. Therefore, any hydro-geomorphologic case is stored in the database if the occurrence led to casualties or injuries, and missing, evacuated or homeless people, independently of the number of people affected. The sources of information are 16 national, regional and local newspapers that implied the analysis of 145,344 individual newspapers. The hydro-geomorphologic occurrences were stored in a database containing two major parts: the characteristics of the hydro-geomorphologic case and the corresponding damages. In this work, the main results of the Disaster database are presented. A total of 1,621 disastrous floods and 281 disastrous landslides were recorded and registered in the database. These occurrences were responsible for 1,251 dead people. The obtained results do not support the existence of any exponential increase in events in time, thus contrasting with the picture provided to Portugal by the Emergency Events Database. Floods were more frequent during the period 1936–1967 and occurred mostly from November to February. Landslides were more frequent in the period 1947–1969 and occurred mostly from December to March.

The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km2) located in the Northern Portugal. In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use) and was compared with a model developed using all seven landslide predisposing factors. Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.