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This paper shows the results of a study aimed at quantitatively estimating—in terms of direct (repair) costs, at large scale (1:5000)—the slow-moving landslide risk to a road network assumed as undamaged as well as the consequences to the same network in damaged conditions. The newly conceived methodological approaches address some challenging tasks concerning (i) the hazard analysis, which is expressed in terms of probability of occurrence of slow-moving landslides with a given intensity level that, in turn, is established based on empirical fragility curves, and (ii) the consequence analysis, which brings to the generation of time-dependent vulnerability curves. Their applicability is successfully tested in a case study in the Campania region (southern Italy) for which both very high-resolution DInSAR data and information gathered from in situ surveys on the severity of damage sustained by the selected road sections are available. Benefits associated with the use of the obtained results in informed decision-making processes are finally discussed.

Landslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units.

Natural disasters often show highly heterogeneous character due to complex geo-environmental settings. The spatial distribution of landslides is generally clustered at different scales. In this paper, we proposed a methodology for landslide susceptibility mapping (LSM) with consideration of spatial correlation and distribution of clustered landslide data. To quantify the spatial correlation of landslides, a normalized spatial-correlated scale index (NSCI) was introduced. Based on the definition of landslide frequency ratio, calibrated landslide potential index (CLPI) was proposed to account for the effect of landslide clustering. Considering the fractal distribution of landslides, the variable fractal dimension model (VFDM) was introduced to measure the spatial association between clustered landslides and conditional factors. Based on the definition of fractal dimension (D), the weights of the factors were obtained from fractal perspective. We proposed a weighted calibrated landslide potential model (WCLPM), obtained by the combination of CLPI values and weights of the factors. The proposed method is illustrated by example in Xinjiang, NW China, where landslide points are clustered at regional scale. In the example, the landslides were randomly split into two groups: one for building landslide model (training dataset) and the other for validating the model (validating dataset). Five landslide conditional factors (lithology, tectonic faults, elevation, slope, aspect) were selected, processed, and analyzed in a geographic information system (GIS) environment. Predictive accuracy of the WCLPM was evaluated and compared based on the calculation of area under the prediction-rate curve (AUPRC). The example shows that the proposed WCLPM provides good prediction for the study area (AUPRC = 0.8700). This study provided a novel and practical method for LSM.

This paper presents a landslide inventory map for the Chittagong Hilly Areas of Bangladesh based on Google Earth and field mapping. We developed a set of criteria to identify landslides in Google Earth and introduced a method to assess the accuracy of mapped landslides when they are recorded as points rather than polygons in the field. In total, 230 landslides (mainly occurred between 2001 and 2016) were mapped in Google Earth. Field mapping identified 548 landslides and most of them occurred during Summer 2017. In total, the inventory includes 730 landslides for Chittagong Hilly Areas from 2001 to 2017. The field validation suggests that the accuracy of mapped landslides using Google Earth varies from 69 to 88%. Field work is preferred to map detailed landslides in or near urban areas, road networks, human settlements, and other accessible areas, while Google Earth has the advantage to map landslides in inaccessible areas. The combination of these two approaches provides a means to prepare the landslide inventory for an entire area.

The present paper reports on the activities conducted since 2008, within the International Programme on Landslides (IPL), for the realisation of the Geological and Landslides Museum of Civita di Bagnoregio (Central Italy) (IPL project 106-1). The town of Civita di Bagnoregio (Latium Region, Central Italy), located at the border of the ancient Volsini volcano and placed on a high tuff peak that rests on fragile clay, is a stunning case where the human presence has tried for centuries to hinder the natural degradation of the cliff. It is a paradigm of the men who tries to survive in a hostile environment with an incomparable beauty and nature that wants to take its course dismantling and eroding all reliefs. The Geological and Landslides Museum is a scientific institution introducing visitors to the fundamentals of landslide science, describing the site evolution through the relationship between human being and geomorphological processes in the last millennium and, finally, introducing to the “real museum” that are the ongoing landslides on the cliff and valleys. The visitors will find in the museum many scenic spots that are describing the evolution of territory but, in the meantime, they are encouraged to visit the site and to touch by hand the various geomorphological features of the site. The museum also seeks to develop a culture of resilience to landslide disasters, promoting the site as a “geomorphological park” to study and test innovative monitoring tools, for the benefit of scientific community.

The purpose of this paper is to analyze landslide risk public awareness and knowledge in Mexico based on people’s common understanding and perception of landsliding associated with previous events. The methodology involved the design and application of a risk perception questionnaire in the municipality of Teziutlan, Puebla, Mexico, an area that has been historically affected by mass movement processes, particularly during a rainfall induced landslide disaster event in October 1999. Sampling framework comprised two boroughs; Downtown Teziutlan (DTT, N = 65), and San Andres neighborhood (SAN, N = 72). The former has no evidence of landslide risk, whereas the latter has been affected by landsliding. The questionnaire included the following items: (1) experience, (2) landslide risk awareness, (3) exposure, (4) preparedness, (5) responsibility, (6) response, and (7) trust. Results suggested that all the inhabitants of the sample are aware of risk of landsliding and consider the dwellings situated in the city center as the safest. The latter is consistent with a landslide susceptibility map on which hillslope material properties are reflected and lithological units of highest resistance are located beneath the city center. The need of undertaking different initiatives to achieve disaster risk reduction at community level was clearly expressed by the respondents in terms of ranking all the actions listed in the questionnaire as of high and moderate priority to improve the security of the residents. Highest accountability for actions in case of a landslide disaster was attached to the Mexican Army, as it is in charge of disaster response nationwide. Responses provided by the municipal government, health institutions and the Red Cross in DTT and by the state and municipal government along with civil protection in SAN were perceived as bad or just sufficient. High level of people’s confidence to get information on landslide disaster preparedness and response from the Red Cross and scientists was also perceived in the two communities. From the outcome of the present investigation, it is possible to conclude and reinforce the argument that a better understanding on how landsliding is perceived is one of the most significant issues for enhancing landslide disaster risk awareness and knowledge and to guarantee the advance of resilient communities at individual and collective scales.

The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning.

The Third World Landslide Forum was held on 2–6 June 2014 at the China National Convention Center, Beijing, China. This article outlines the background and the objectives of the World Landslide Forums and reports on the organized sessions and published books for the Third World Landslide Forum. During this triennial event of ICL, World Centres of Excellence on Landslide Risk Reduction and IPL Awards for Success, Varnes Medal, Best paper awards, and others were conferred. Through the examination in the high-level panel discussion on the first day and the roundtable discussion on the final day, 2014 Beijing Declaration “Landslide Risk Mitigation: Toward a Safer Geo-environment” was adopted. It is a document calling to join the planned Sendai partnerships 2015–2024 as a contribution to post-2015 Framework for Disaster Risk Reduction.

This paper presents rainfall-induced landslide thresholds and predicts landslide hazard in Kuala Lumpur metropolitan city and surrounding areas. Landslide events from 2000 to 2012 were collected. The long and short antecedent rainfall days were prepared for landslide and non-landslide days simultaneously. First, threshold analysis was conducted by using data obtained from rainfall stations located in highly urbanized areas of Kuala Lumpur metropolis. Six rainfall gauges were selected, and the study area was divided into six zones according to rainfall gauges: Taman Desa Station (TD-KL), Genting Klang (GK-KL), LDG Edinburgh Station (LDGE-KL), SG Raya Hulu Langat Station (SRHL-Slg), Puchong Drop Station (PD-Slg), and Bukit Antarabangsa (BTA-Slg). After the threshold analysis was conducted for different periods (10, 15, and 30 days) in each station, reliability index test was conducted to optimize the best threshold that limits the predicted events along the study period for each region. Second, the threshold analysis results were used as input in the Poisson probability model to estimate landslide temporal probability (P T). Third, the spatial probability (P S) analysis was prepared by using the evidential belief function multiplied by the P T to obtain the hazard maps for 1-, 3-, and 5-year scenarios. Finally, a validation process was conducted to test the prediction performance of the resultant hazard map for a 1- and 2-year prediction by using the landslide inventory of 2012 to early 2014, which was not included in the modeling of the hazard map. Results showed a valid correlation between the high and moderate hazardous areas for the six zones. The predicted hazard maps indicated a quantitative assessment of the prone areas and proved to be a valid disaster management tool. The produced hazard maps may play a vital role as input component in risk analysis.

Development of landslide inventories based on remote sensing techniques has become one of the main tools in assessment of hazards and risk. Among those techniques, visual and automatic and semi-automatic analysis of high- and very high-resolution (VHR) satellite images, or a combination of these, has recently been considered as a promising way to identify and map landslides at local and regional scales. In this context, a landslide inventory for the municipality of Pahuatlan, Puebla, in central Mexico was prepared by combining three techniques: (1) visual analysis of stereoscopic pairs of VHR satellite images (GeoEye-1), (2) visual analysis of monoscopic VHR satellite images (SPOT 5 and Google Earth images), and (3) field surveying. In this paper, particular attention is given to landslide identification and mapping based on the GeoEye-1 stereo-pairs. Additionally, as a preliminary step in the use of VHR imagery, a general review is presented of the available VHR satellite images, software and hardware that can be useful for digital mapping of landslides. The landslide inventory included a total of 577 landslides, corresponding to an average density of 10.5 landslides per km2. Of these, 385 were classified as recent, 171 as old, and 21 as very old, regardless of state of activity. The total mapped area was 54.9 km2; 57.7 % of it had been affected by landsliding. The mean area occupied by recent landslides was of the order of 1,066 m2; for old landslides, it was 82,559 m2 and for very old landslides 1,173,952 m2. Debris flows were the most frequent type of movement (217), followed by 167 translational slides, 97 complex movements, 79 rotational slides, and 17 falls and topples. The cost–benefit relationships of a number of these techniques remain debatable because of the high cost of some of the VHR images and the related software and hardware. However, the appearance of new satellite sensors is likely to generate market competence, so this type of image will probably be available at a much lower cost in the near future. Additionally, it is important to consider that the use of several stereo-high-resolution images involves no cost, as downloading high-resolution images from Google Earth, using Google Earth Pro is currently available. The relative rapidity of these techniques can be highly valuable after a regional landslide disaster has occurred, since damage to roads and infrastructure usually prevents the rapid and accurate evaluation of the impact of landsliding. Most importantly, these techniques can be of great value for hazard evaluation of potentially unstable inhabited slopes.

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans.

The International Consortium on Landslides (ICL) was established in 2002 and soon became an important world player in the field of landslide risk mitigation; in 2012, it has more than 50 member organizations throughout the world. As a part of the activities to celebrate ICL’s tenth anniversary, a strategic document for the second decade called the ICL Strategic Plan 2012–2021 with the motto “to create a safer geo-environment” was adopted in Kyoto in January 2012. An important part of this document was a clear dedication to broaden the scope and societal impact in a thematic, institutional, and geographic manner. Thus, thematic and regional networks for landslide risk reduction were recognized to be an important form of ICL activities in the decade to come. In this paper, one of the eight newly established thematic and regional ICL networks is presented: the ICL Landslide Monitoring and Warning Thematic Network, a joint effort of ten ICL member organizations and two ICL supporters from eight countries, five of them are active ICL World Centres of Excellence in Landslide Risk Reduction in the period 2011–2014. The general objective of this thematic network is to compare experiences in the field of landslide monitoring and existing/installed (early) warning systems for active landslides in different regions of the world.

The paper proposes a methodology for quantitative landslide risk assessment for regional-scale analysis. Each component of risk, i.e., hazard, vulnerability, and consequence analysis, is quantitatively assessed. The developed landslide risk assessment methodology is tested in Kumluca watershed, in Bartin, Turkey. Geographic information systems and remote sensing techniques are used to create landslide factor maps, to obtain susceptibility maps, hazard maps, elements at risk, and risk maps. Susceptibility maps are obtained by using a logistic regression model while adopting a grid-based mapping unit. In addition to spatial probability of occurrence of damaging events, landslide hazard calculation requires the determination of the temporal probability. Precipitation triggers the majority of landslides in the study region. The critical rainfall thresholds were estimated by using antecedent rainfalls and landslide occurrence dates based on Gumble Distribution approach. The elements at risk are extracted from existing digital cadastral databases and the vulnerabilities are obtained by adopting some generalization approaches. To conclude, quantitative risk maps were produced on a continuous scale where numerical values indicate the distribution of risk including the annual probability of expected losses in TL per pixel and the annual probability of life loss per pixel for property and life, respectively. For the considered case study, it is found that the annual probability of property loss is the highest for the provincial highway and the provincial road. The property loss map highlights that the annual expected loss to power network is medium. The annual probability of life loss map illustrates that the region surrounded by Kumluca town, Kizillar, and Zafer villages have medium and high annual expected loss of population values, respectively.

This paper provides a detailed account of the landslide that took place between 09:00 and 10:00 am on April 25, 2009, near the village of Rampac Grande in northern Peru (department of Ancash). Lives were lost and property destroyed during the event. Archive information, remotely sensed data, and detailed field investigations including sampling were applied to describe the events. Historically, landslides have been a common problem in this region with recorded events dating as far back as the 1800s. The landslide is considered as a deep-seated composite rotational–translational earth slide–earth flow. The local community suspected that a mining company had triggered the landslide as it prospected for precious metals. No evidence was found to suggest that anthropogenic activity caused the landslide. Instead, it was likely caused by the considerable amount of cumulative precipitation during the annual rainy season. Several possible sources of future landslide reactivation have been identified. Finally, a poster has been produced for the local community providing basic information about the event and how to manage the potential future landslide hazard. It is hoped that this simple method of knowledge dissemination will provide a fundamental bridge between the efforts of research scientists and the needs of the local community.

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.

This paper presents the preliminary results of a geomorphological survey of the Olvera area (Cadiz province, Betic Ranges, Spain) and the use of the Geographic Information System (GIS) Open Source (OS) software plus Database Management System (DBMS) for making available and distributing the landslide data over the Web. In the geomorphologic survey, different landforms have been identified in the area, including structural, anthropogenic, fluvial, karst, and slope forms. In particular, the majority of the slope forms are complex (from topple to rotational slides and falls), but there are also minor forms like debris flows and mudslides. To manage geomorphological data, an Open Source GIS was used, which contained the following components: QuantumGIS, System for Automated Geoscientific Analyses (SAGA), GIS and Geographic Resources Analysis Support System (GRASS), GIS for Digital Elevation Model (DEM) generation. A key aim was to make the project-derived data available over the Web. This was achieved using MapServer which allows for the representation of the derived geospatial data with pMapper providing the graphical Web interface. Our study highlights the process dynamics of run-off erosion in Olvera derived through the use of advanced computer-based mapping tools. The resulting map products and interpretations are available via the Internet. To date, derivative maps have been produced to improve maintenance of roads and transport and of the construction of new infrastructure.

In 2007 and 2008, six big damaging rockfalls occurred at four sites in Wu Gorge, the second gorge of the Three Gorges, China. Detailed surveys and aerial-photographic interpretation identified 104 potentially dangerous rock masses. This paper reviews previous rock-mass risk ratings, examines dangerous rock-mass structure and presents a new assessment system for rockfall risk (ASRFR) in the Wu Gorge area. The ASRFR considers 15 factors: seven factors for hazard and eight factors for consequence. Relative importance weights for these factors are ascertained using an analytic hierarchy process. Using an equation to calculate the risk, the 104 dangerous rock masses were divided into three risk groups: high risk (33 sites), medium risk (33 sites) and low risk (38 sites). The ASRFR analysis can be used to divide the shipping route through the Wu Gorge into seven courses each of one of three classes: safe-route regions, yellow-alarm regions and red-alarm regions. The system provides geological information and a rockfall-risk management tool for local government and the shipment-route department.

Landslide risk assessment (LRA) is a key component of landslide studies. The landslide risk can be defined as the potential for adverse consequences or loss to human population and property due to the occurrence of landslides. The LRA can be regional or site-specific in nature and is an important information for planning various developmental activities in the area. LRA is considered as a function of landslide potential (LP) and resource damage potential (RDP). The LP and RDP are typically characterized by the landslide susceptibility zonation map and the resource map (i.e., land use land cover map) of the area, respectively. Development of approaches for LRA has always been a challenge. In the present study, two approaches for LRA, one based on the concept of danger pixels and the other based on fuzzy set theory, have been developed and implemented to generate LRA maps of Darjeeling Himalayas, India. The LRA map based on the first approach indicates that 1,015 pixels of habitation and 921 pixels of road section are under risk due to landslides. The LRA map derived from fuzzy set theory based approach shows that a part of habitat area (2,496 pixels) is under very high risk due to landslides. Also, another part of habitat area and a portion of road network (7,204 pixels) are under high risk due to landslides. Thus, LRA map based on the concept of danger pixels gives the pixels under different resource categories at risk due to landslides whereas the LRA map based on the concept of fuzzy set theory further refines this result by defining the degree of severity of risk to these categories by putting these into high and low risk zones. Hence, the landslide risk assessment study carried out using two approaches in this paper can be considered in cohesion for assessing the risks due to landslides in a region.

A digital landslide database has been created for Nicaragua to provide the scientific community and national authorities with a tool for landslide-hazard assessment, emergency management, land-use planning, development of early warning systems, and the implementation of public and private policies. The Instituto Nicaraguense de Estudios Territoriales (Nicaraguan Geosciences Institute, INETER) began to compile the database in a digital format in 2003 as part of a comprehensive geographical information system for all types of geohazards. Landslide data have been obtained from a variety of sources including newspapers, technical reports, and landslide inventory maps. Inventory maps are largely based on fieldwork and aerial-photo analyses conducted by foreign development agencies in collaboration with INETER and other Nicaraguan institutions. This paper presents the sources of landslide information, introduces the database, and presents the first analyses of the data at national and regional scales. The database currently contains spatial information for about 17,000 landslides that occurred in mountainous and volcanic terrains. Information is mainly recorded for the period 1826–2003, with a large number of events that occurred during the disastrous Hurricane Mitch in October 1998. The oldest historical event is dated at 1570, some events are recorded as prehistorical, and other events have unknown dates of occurrence. Debris flows have been the most common types of landslides, both in volcanic and nonvolcanic areas, but other types, including rockfalls and slides, have also been identified. Intense and prolonged rainfall, often associated with tropical cyclones, and seismic and volcanic activity represent the most important landslide triggers. At a regional scale, the influence of topographic (elevation, slope angle, slope aspect) and lithologic parameters on the occurrence of landslides was analyzed. The development of the database allowed us to define the state of knowledge on landslide processes in the Nicaragua and to provide a preliminary identification of areas affected by landslides.

The results of the first investigation on historical landslides that occurred in Nicaragua before 1990 are presented. The paper shows how the analysis of historical documents can provide important information about the type and the temporal and spatial distribution of landslide events, especially for countries where landslide investigation is a young discipline. The methodology included research of historical sources at national institutions, public libraries, international journals, the Internet, and the collection of original texts from newspapers and their transcription in analog format. Analysis and interpretation of the data was performed, followed up by the digitalization of landslide spatial locations using the software ArcGIS and the integration of the landslide attributes in a structured query language (SQL) server database. Finally, the analysis of the stored data was done using SQL interactive queries. The type and quality of the historical sources available are described. Limitations and problems, common in the preparation of any historical catalogue, and possible sources of error derived from the collection, interpretation, and storage of the data are discussed as well. One hundred thirty-five historical landslides were identified. The study shows that these events occurred mostly in combination with other natural phenomena. The collected data were used to construct a geo-referenced inventory of historical landslide events, including information about damage and human losses caused by them. The inventory provides the basis for a more comprehensive landslide hazard assessment in Nicaragua.

The Island of ‘La Desirade‘ is severely affected by rock falls. One house located in the area of ‘Le Souffleur‘ was partly destroyed by blocks in 1997, with two people injured. About 50 people still live under a dangerous cliff along the road from Beausejour to Baie-Mahault, the two main villages of this eastern end of Guadeloupe archipelago. The first solution proposed by the state based on relocation of few houses was faced to population hostility. Solutions had to be reshaped and designed based on a global assessment of the different hazards affecting the Island (earthquakes, cyclones, flooding), on protection works taking into account the fact that French regulation prohibits new constructions behind protection fences, and on global and local land-use development. Risk management process has included technical analysis, social behaviour, cultural heritage of building and development, responsibility and regulation, short-term and long-term financing, land-use development strategy as well as information and communication. The solutions that are now being implemented seem to give satisfaction to all the partners. This example of landslide risk management carried out in La Desirade is discussed in this paper.

This paper explains the procedure for the generation of a landslide risk index map at national level in Cuba, using a semi-quantitative model with ten indicator maps and a cell size of 90 × 90 m. The model was designed and implemented using spatial multi-criteria evaluation techniques in a GIS system. Each indicator was processed, analysed and standardised according to its contribution to hazard and vulnerability. The indicators were weighted using direct, pairwise comparison and rank-ordering weighting methods, and weights were combined to obtain the final landslide risk index map. The results were analysed per physiographic region and administrative units at provincial and municipal levels. The Sierra Maestra mountain system was found to have the largest concentration of high landslide risk index values while the Nipe–Cristal–Baracoa system has the highest absolute values, although they are more dispersed. The results obtained allow designing an appropriated landslide risk mitigation plan at national level and to link the information to the national hurricane early warning system, allowing also warning and evacuation for landslide-prone areas.

Recently, design of public works in a landslide area trends to demand evaluation for landslide susceptibility from the viewpoint of prevention against disasters. A conventional method for this evaluation is carried out in the following manner. The first step is collection of data which characterize the feature of a landslide area with the aid of field survey, and secondly each area is empirically ranked for landslide susceptibility. However, this method requires such experience for landslide that of ten givse a different result of ranking in correspondence to the degree of experience. On the other hand, this paper proposes a new method applying the quantification theory to categorical data which are collected from field survey. A region of Shikoku district in Japan was dealt with, an evaluation map was obtained showing land slide susceptibility, and comparison was made between the present method and the conventional method. The result convictsus the reliability of the present method for evaluation of landslide susceptibility.