Landslide Database Research Articles

Waldentwicklung und flachgründige Rutschungen: eine grossflächige GIS-Analyse

Forest dynamics and shallow landslides: a large-scale GIS-analysis Through continuous and abrupt changes in the forest and particularly in its root area, the disposition for shallow landslides also changes. The aim of this article is to learn more about the relationships between long-term forest dynamics and the risk of landslides with the help of large spatial data sets. A total of 6670 shallow landslides were selected from the natural event register StorMe, the event register of the Canton of Obwalden and the WSL database for shallow landslides and slope debris flows and overlaid with GIS data on the current and former forest situation (Siegfried maps of approx. 1880) and with windthrow areas (Vivian and Lothar). Shallow landslides occurred less frequently in the forest than outside of it at inclinations of less than 35 degrees. Higher landslide densities occurred in coniferous and mixed forests (compared to deciduous forests) and in relatively young forests (ingrown after 1880). Landslides in younger forests occurred on slopes less steep than in older forests. More shallow landslides occurred on former windthrow areas during the first 3 to 17 years after the event, however these landslides occurred on comparatively steep slopes. The analyses show that the disposition towards shallow landslides defined by slope inclination and subsoil can change strongly due to reforestation processes and natural disturbances. Silvicultural interventions may contribute to an improvement of the protective effect in the long term if they are carried out carefully and if they contribute to an increased adaptability to natural disturbances.

Pesantren-based disaster mitigation strategy : case study pondok pesantren Darunnajah Cipining Bogor

This study aims to analyze the pesantren-based drought disaster mitigation strategy. This research method is a descriptive research method with a qualitative approach to describing drought disaster mitigation in pesantren. To get the right and appropriate informants, the sampling technique in this study used a purposive sample system. Data collection is done by using interview techniques, documentation, literature study, and observation. Mitigation in this research refers to mitigation theory which divides mitigation into 2 (two) forms of strategy, namely: first, structural mitigation, in the way of infrastructure development as a driver to minimize the impact of disasters through the use of technological approaches. The symptoms observed were a compilation of a database of potential landslides and the creation of an early warning system. Second, non-structural mitigation, in the form of management and spatial training to improve community capacity. This research concludes: Mitigation of drought in the Cipunn Darunnajah Islamic Boarding School in Bogor is carried out structurally and non-structurally. Structural mitigation is done by compiling a database of potential hazard areas and installing an Early Warning System (EWS). Non-structural mitigation is carried out by providing information, outreach and training and disaster simulation. Efforts have been made to improve the effectiveness of disaster mitigation through the establishment of Disaster Response Students and Disaster Resilient Alumni.

Schützt der Wald vor Rutschungen? Hinweise aus der WSL-Rutschungsdatenbank

Does the forest provide protection from landslides? Evidence from the WSL Shallow Landslide Database During strong rainfall events, shallow landslides and debris avalanches (hillslope debris flows, or open-slope debris flows) are triggered and sometimes lead to considerable damage. Analysis of damage-causing events show that there are fewer landslides in forested areas compared to non-forested areas, which indicates the generally positive influence of forest vegetation on slope stability. However, these effects depend on the condition of the forest stand and quantification of the effects is difficult. Event documentation contributes to a better understanding of the relevant processes. The information obtained is not only important for the preparation of hazard maps, but also provides valuable insight for assessing the hazard protection provided by the forest. Data from the landslide database of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) were used to evaluate the influence of the forest on slope stability. Currently, the database contains information on 734 landslides. Of these, 661 were included in the evaluation – 356 landslides in non-forested areas and 305 in forested areas. In areas with slope angles up to 38°, more landslides per unit area are observed in non-forested areas than in forested areas. In areas with steeper slope angles a stabilizing effect of the forest is no longer recognizable. Statistical analyses show that landslides in forested areas are smaller than in non-forested areas and are more frequent on steeper slopes. In general, the landslides become smaller with increasing slope. Multivariate analyses indicate a positive influence of the forest and also somewhat smaller landslides in well-developed forests. Negative effects are evident in non-forested areas and in areas with overly dense forests. In addition to illustrating the importance of the forest condition for slope stability, the paper also discusses how the forest condition can be described.

Factor analysis for the statistical modeling of earthquake-induced landslides

Earthquake-induced landslides are difficult to assess and predict owing to the inherent unpredictability of earthquakes. In most existing studies, the landslide potential is statistically assessed by collecting and analyzing the data of historical landslide events and earthquake observation records. Unlike rainfall-induced landslides, earthquake-induced landslides cannot be predicted in advance using real-time monitoring systems, and the development of the models for these landslides should instead depend on early earthquake warnings and estimations. Hence, in this study, factor analysis was performed and the frequency distribution method was employed to investigate the potential risk of the landslides caused by earthquakes. Factors such as the slope gradient, lithology (geology), aspect, and elevation were selected and classified as influential factors to facilitate the construction of a landslide database for the area of study.

Estimation of Rainfall-Induced Landslides Using the TRIGRS Model

Rainfall-induced landslides have become the biggest threat in the Indian Himalayas and their increasing frequency has led to serious calamities. Several models have been built using various rainfall characteristics to determine the minimum rainfall amount for landslide occurrences. The utilisation of such models depends on the quality of available landslide and rainfall data. However, these models do not consider the effect of local soil, geology, hydrology and topography, which varies spatially. This study is to analyse the triggering process for shallow landslides using physical-based models for the Indian Himalayan region. This research focuses on the utilisation and dependability of physical models in the Kalimpong area of Darjeeling Himalayas, India. The approach utilised the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model, which is a widely used model in assessing the variations in pore water pressure and determining the change in the factor of safety. TRIGRS uses an infinite slope model to calculate the change in the factor of safety for every pixel. Moreover, TRIGRS is used to compare historical rainfall scenarios with available landslide database. This study selected the rainfall event from 30th June to 1st July 2015 as input for calibration because the amount of rainfall in this period was higher than the monthly average and caused 18 landslides. TRIGRS depicted variations in the factor of safety with duration before, during and after the heavy rainfall event in 2015. This study further analysed the landslide event and evaluated the predictive capability using receiver operating characteristics. The model was able to successfully predict 71.65% of stable pixels after the landslide event, however, the availability of more datasets such as hourly rainfall, accurate time of landslide event would further improve the results. The results from this study could be replicated and used in other unstable Indian Himalayan regions to establish an operational landslide early warning system.

Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks

This study presents r.landslide, a free and open source add-on to the open source Geographic Information System (GIS) GRASS software for landslide susceptibility mapping. The tool was written in Python language and works on the top of an Artificial Neural Network (ANN) fed with environmental parameters and landslide databases. In order to illustrate the application and effectiveness of the developed tool, a case study is presented for the municipality of Porto Alegre, Brazil. The resulting landslide susceptibility maps are compared with the map published by the Brazilian Geological Survey (CPRM) and a direct comparison using unseen (new) landslide records indicate that the r.landslide can identify and pinpoint susceptible areas with better accuracy. The module can be used by natural disaster management bodies and land use planning organs as a support tool for the elaboration of landslide susceptibility maps in an agile and efficient manner.

Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D−0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

A comprehensive global database of giant landslides on volcanic islands

The first comprehensive global database of giant landslides on volcanic islands is outlined in this report. This database comprises a total of one hundred and eighty-two entries: the Atlantic Ocean hosts seventy-five giant landslides; the Pacific Ocean hosts sixty-seven giant landslides; and the Indian Ocean hosts forty giant landslides. To determine the spatial characteristics of each giant landslide, it has been necessary to georeference published maps using ArcGIS software coupled with global DTMs. Using the georeferenced outputs, it has been possible to measure the basic morphometric characteristics of each landslide such as its length, width, perimeter, area, and fall height. Landslide volumes have been calculated with a higher degree of certainty in thirty-five cases and with a lower degree of certainty in sixty-three cases while complete outlines of the landslide area have been defined in ninety-six cases. On the basis of these data, it has been possible to interrogate relationships between potentially significant variables. The age distribution of giant landslides on volcanic islands demonstrates that more than half of the records in the database occurred during the last 0.5 Ma. This global database of giant landslides on volcanic islands is hosted on the website of the Institute of Rock Structure & Mechanics: https://www.irsm.cas.cz/ext/giantlandslides . From there, the records can be downloaded as a spreadsheet or as a kml file for interrogation in a number of geospatial software programs including ArcGIS and Google Earth. This work is part of the activities of the International Consortium on landslides, namely, its International Programme on Landslides (Project No. 212).

Susceptibility of reservoir-induced landslides and strategies for increasing the slope stability in the Three Gorges Reservoir Area: Zigui Basin as an example

The construction of the huge Three Gorges reservoir affected a large region, and the resultant geological and environmental impacts have caused global concern. The remarkable, 30-m annual fluctuation in the reservoir water level poses a significant threat to slope stability in this area. Four hundred sixty-two landslides were identified in the Zigui basin using historical records, satellite images, field investigations and unmanned aerial vehicle (UAV) observations, enabling the construction of a complete landslide database and distribution map. Three failure modes of landslides in the Zigui basin are used to illustrate the major factors that govern reservoir-induced landslides. The results show that (1) > 99% of identified landslides occur on slopes angle <47°, while >80% occur at elevations below 600 m; (2) Jurassic Niejiashan Formation is highly prone to landslides; (3) Low reservoir levels of 145 m to 155 m greatly reduce slope stability. Based on the information entropy method that use the conditional probability of different influencing factors and principal component analysis as inputs, the relative contributions of various influencing factors are quantified and a landslide susceptibility map was drawn. This susceptibility map helps define the countermeasures that will best reduce fatalities and property losses for areas having different landslide conditions and susceptibilities.

FREQUENCY RATIO LANDSLIDE SUSCEPTIBILITY ESTIMATION IN A TROPICAL MOUNTAIN REGION

Abstract. In a high-rainfall, landslide-prone region in this tropical mountain region, a landslide database was constructed from high resolution satellite imagery (HRSI), local reports and field observations. The landslide data was divided into training (80%) and validation sets (20%). From the digital elevation model (DEM), scanned maps and HRSI, twelve landslide conditioning factors were derived and analysed in a GIS environment: elevation, slope angle, slope aspect, plan curvature, profile curvature, distance to drainage, soil type, lithology, distance to fault/lineament, land use/land cover, distance to road and normalized difference vegetation index (NDVI). Landslide susceptibility was then estimated using the frequency ratio method as applied on the training data. The detailed procedure is explained herein. The landslide model generated was then evaluated using the validation data set. Results demonstrate that the very high, high, moderate, low and very low susceptibility classes included an average of 86%, 7%, 4%, 3% and 1% of the training cells, and 84%, 7%, 5%, 3% and 1% of the validation cells, respectively. Success and prediction rates obtained were 90% and 89%, respectively. The sound output has discriminated well the landslide prone areas and thus may be used in landslide hazard mitigation for local planning.

Global detection of rainfall-triggered landslide clusters

Abstract. An increasing awareness of the cost of landslides on the global economy and of the associated loss of human life has led to the development of various global landslide databases. However, these databases typically report landslide events instead of individual landslides, i.e., a group of landslides with a common trigger and reported by media, citizens and/or government officials as a single unit. The latter results in significant cataloging and reporting biases. To counteract these biases, this study aims to identify clusters of landslide events that were triggered by the same rainfall event. An algorithm is developed that finds a series of landslide events that (a) is continuous with no more than 2 d between individual events and where (b) precipitation at the location of an individual event correlates with precipitation of at least one other event. The developed algorithm is applied to the Global Landslide Catalog (GLC) maintained by NASA. The results show that more than 40 % of all landslide events are connected to at least one other event and that 14 % of all studied landslide events are actually part of a landslide cluster consisting of at least 10 events and up to 108 events in 1 d. Duration of the detected clusters also varies greatly from 1 to 24 d. Our study intends to enhance our understanding of landslide clustering and thus will assist in the development of improved, internationally streamlined mitigation strategies for rainfall-related landslide clusters.

Landslide database and susceptibility map updating of a test area in the Serchio River basin (northern Tuscany, Italy)

Extreme rainfall events more and more often affect different regions of Italy. An example of area frequently hit by such events is the Serchio River basin (northern Tuscany), in which floods and landslides commonly occur. For example, the 21–22 July 2014 rainfall induced floods and tens of shallow landslides in the Freddana Torrent valley (a sub-basin of the Serchio River), causing severe damage toroads and population.The aims of this work is to update the landslide inventory and the landslide susceptibility map of a portion (7.1 km2) of the Freddana Torrent valley. The landslide susceptibility map was updated according to the guidelines proposed by the Authority of Serchio River Basin.The new susceptibility map allowed to identify potentially unstable areas basing on the landslide index of the geological formations and on some morphological features of the study area. The new map and the previous one were compared highlighting the changes as regards the individuation of the areas more prone to failures.

Detailed mapping of shallow landslides in eastern Oklahoma and western Arkansas and potential triggering by Oklahoma earthquakes

Landslides have long been considered one of the main erosive drivers that sculpt hillslopes over time. Tectonic forces raise slopes that can be sculpted by climate and the degree to which they can be sculpted depends upon the underlying lithology and hillslope geomorphology. Landslides destroy life and property and contribute to the evolution of mountain landscapes by generating and transporting hillslope sediments. In this study, we investigate the dynamics of soil-mantled hillslopes in eastern Oklahoma and western Arkansas, USA, by mapping shallow landslides, evaluating the frequency and magnitude of the landslides, and quantifying the surface geometries.We mapped 185 shallow landslides in a 150 km2 area. The approximate ages of landslides and the frequencies and magnitudes (area, volume and runout) were estimated using historical aerial photographs. The landslide database was then used to compute rate of landslide occurrence and sediment yield. In addition, landslide surface geometries, including slope, curvature and surface roughness, were determined from 10 m NED and 2 m LiDAR elevation data. The hillslope susceptibility studies, derived from elevation data, were augmented by our review of common historical accounts that link landslides in the study region to the 1952 M 5.4 El Reno, Oklahoma earthquake ~300 km away. In light of that case study, we determined the frequency of historical earthquakes that impart >2 kPa dynamic stresses for the study area since 1900 and find an increased potential for dynamic stress perturbation of landslide activity since ~2010. All these characteristics were then evaluated to understand the dynamics of hillslopes and hillslope susceptibility to shallow landslides.Frequency assessment of mapped landslides suggested that frequency of landslides increased significantly during 2005–2016 compared to 1995–2005. On average ~10 shallow landslides occurred every year during 2005–2016. These landslides alone contributed ~1.5 × 105 m3/yr (sediment yield rate: ~0.10 cm/yr) of sediments. Surface roughness values were relatively higher in landslide areas compared to that of surrounding non-landslide areas. The surface roughness index mapped ~84% of observed landslides, and suggests that the index could be used to map hillslope susceptibility to landslides for future planned studies.

Visual analysis of the evolution and focus in landslide research field

This paper analysed the evolution of landslide research and research foci in different countries. The data comprise 3105 landslide SCI articles published between January 1977 and June 2015 from the Web of Science. The data are extracted under interaction constraints of the journal title, category, and keywords. The complex network method is used for the analysis. First, from the perspective of topics and methods, the evolution is systematically assessed by generating a co-citation network of the articles and a semantic cluster analysis. Second, from the perspective of topics and landslide-related disasters, the focus in different countries is discussed by generating co-occurrence networks. These networks are the co-occurrence of the countries and keywords, and the co-occurrence of countries and landslide-related disaster phrases. The main conclusions are as follows: (1) landslide susceptibility analysis and methods of machine learning are popular research topics and methods, respectively. The topics change through time, and the article output is influenced by increasing landslide-related disasters, increasing economic losses and casualties, a desire for a more complete and accurate landslide inventory, and the use of effective methods, such as geographical information Science (GIS) and machine learning. (2) The research focus in each country is related with the country-specific disasters or economic costs caused by landslides to some degree. In addition to Italy and the USA, China is the country most commonly affected by landslides, and it should develop its own landslide database and complete in-depth studies of disaster mitigation.

Empirical rainfall thresholds for the triggering of landslides in Asturias (NW Spain)

Landslides are one of the most serious geomorphological hazards in Asturias (NW Spain), where their temporal forecasting constitutes a key issue. The present work uses 559 records from the Principality of Asturias Landslide Database (BAPA) and daily precipitation data series from six rain gauges, gathered during a period of 8 hydrological years (2008–2016), to calculate empirical antecedent rainfall thresholds for the triggering of landslides. The methodology includes (i) the selection of a representative input dataset and (ii) the assessment of the performance of the thresholds through contingency tables and skill scores. On this basis, six local rainfall thresholds for different areas within Asturias have been calculated and compared, allowing progress towards a better understanding of the rainfall-landslides relationship in the NW of Spain. The analysis has highlighted the strong influence of (i) the climatic variability between areas and (ii) the different seasonal precipitation patterns on the landslide-triggering conditions. The antecedent rainfall plays a key role during the wet period while the intensity of the rainfall event is the most relevant factor during the dry period. These observations must be considered to successfully address the temporal forecasting of landslides.

Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides

In some rainfall-triggered landslides, intensity-duration thresholds can have limited prediction ability; therefore, investigation of alternative approaches that can be used for temporal prediction of rainfall-induced landslides is needed. This paper presents a methodology for predicting rainfall-induced shallow landslides based on a lumped conceptual hydrological model. The production storage level during the rainfall event and the rainfall sum during the event are used for landslide prediction. Based on these two hydro-meteorological variables a threshold is defined that could be used for rainfall-induced landslides prediction as part of an early warning system. The presented methodology is tested using the meso-scale Selška Sora River catchment case study in western Slovenia where 20 active landslides from the Slovenian National Landslide Database are used to calibrate and evaluate the methodology performance. The results are compared to three different (i.e. local, regional, and global) intensity-duration thresholds. The results of the presented approach are superior in terms of several goodness-of-fit criteria compared to tested local and global ID thresholds. Because only daily rainfall, evapotranspiration, and discharge data are needed to calibrate the selected hydrological model and only daily rainfall and evapotranspiration to run the model, the presented approach could also be useful for data-scarce areas where detailed physically based landslide prediction models that require many data cannot be constructed. Moreover, we have also derived the probabilistic version of the proposed threshold for triggering of shallow landslides using copula functions.

The human cost of global warming: Deadly landslides and their triggers (1995–2014)

Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.

The Application of the Hybrid GIS Spatial Multi-Criteria Decision Analysis Best–Worst Methodology for Landslide Susceptibility Mapping

The main goal of this article is to produce a landslide susceptibility map by using the hybrid Geographical Information System (GIS) spatial multi-criteria decision analysis best–worst methodology (MCDA-BWM) in the western part of the Republic of Serbia. Initially, a landslide inventory map was prepared using the National Landslide Database, aerial photographs, and also by carrying out field surveys. A total of 1082 landslide locations were detected. This methodology considers the fifteen conditioning factors that are relevant to landslide susceptibility mapping: the elevation, slope, aspect, distance to the road network, distance to the river, distance to faults, lithology, the Normalized Difference Vegetation Index (NDVI), the Topographic Wetness Index (TWI), the Stream Power Index (SPI), the Sediment Transport Index (STI), annual rainfall, the distance to urban areas, and the land use/cover. The expert evaluation takes into account the nature and severity of the observed criteria, and it was tested by using two scenarios: the different aggregation methods of the BWM. The prediction performances of the generated maps were checked by the receiver operating characteristics (ROCs). The validation results confirmed that the areas under the ROC curve for the weighted linear combination (WLC) and the ordered weighted averaging (OWA) aggregation methods of the MCDA-BWM have a very high accuracy. The results of the landslide susceptibility assessment obtained by applying the proposed best–worst method were the first step in the development of landslide risk management and they are expected to be used by local governments for effective management planning purposes.

Landslide Susceptibility Estimation Using GIS. Evritania Prefecture: A Case Study in Greece

Nowadays, natural hazards constitute an integral part in the everyday reality of people’s lives. A landslide event, although usually occurring at a low frequency (compared to other hazards), may develop into a major natural disaster involving extensive and adverse effects, both in the natural and man-made environment. Thus, by making this assumption and combining it with the human mentality that has the tendency to reassure and resist extreme physical processes, the underlying danger “in total”, is multiple of what is expected. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic and geomorphologic characteristics of the region, the purpose of this study was the landslide hazard assessment by using Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method in the Evritania prefecture. At first, landslides occurring in Evritania prefecture, were identified using a landslide database from Institute of Geology & Mineral Exploration of Greece and by primary field studies. The influence landslide factors used in this study were slope, aspect, elevation, lithology, precipitation, land cover, distance from faults and distance from rivers, were obtained from different sources and maps. Using these factors and the identified landslides, the fuzzy membership values were calculated by frequency ratio. Then, to account for the importance of each of the parameters in the landslide susceptibility, weights of each factor were determined based on the Analytical Hierarchy Process method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method and at the end—for computing prediction accuracy—the produced map was verified by comparing to existing landslide locations. These results indicate that the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility area map, about 50% of the occurred landslide fall into high and very high susceptibility zones and also approximately 21% of them indeed located in the low and very low susceptibility zones.

НАЦІОНАЛЬНА БАЗА ДАНИХ ЗСУВНИХ ПРОЦЕСІВ: ПРИНЦИПИ РОЗРОБКИ, УПРОВАДЖЕННЯ ТА ЗАСТОСУВАННЯ ДЛЯ ОЦІНКИ ЗСУВНОЇ НЕБЕЗПЕКИ РЕГІОНАЛЬНОГО І ЛОКАЛЬНОГО РІВНЯ

The main approaches and principles of the development and formation of the database of lanslide processes within the territory of Ukraine are described. Among natural risks one of the most common are landslide phenomena, which lead to significant social and economic losses. Ukraine is characterized by active landslide processes within different structural-tectonic and landscape-climatic zones, which requires a unified approach to the development of a system of their assessment, inventory and monitoring with the subsequent forecasting, and assessment of the impact on manmade objects. The national landslide database is the most comprehensive source of information on the distribution of landslides in different regions of the country, and the main task is the input, processing, analysis and updating of information on landslides. The national landslide database consists of a series of tables stored and maintained in the relational database Oracle or PostgreSQL (open-source database). Use of ArcGIS10 provides the ability to display spatial information, store positional and non-positional attributive landslide data, and perform spatial analysis and modelling operations with the purpose of further prediction of landslide hazard at the regional and local levels.