Parametrical landslide modeling for the hydrogeological susceptibility assessment: from the Crati Valley to the Cavallerizzo landslide (Southern Italy)

The article deals with a tool for landslides susceptibility assessment as a function of the hydrogeological setting at different scales. The study has been applied to a test area located in Southern Italy. First, a 3D groundwater flow model was implemented for a large-scale area. The simulation of several groundwater conditions compared with the landslide activity map allows drawing a hydrogeological susceptibility map. Then, a slope scale analysis was carried out for the Cavallerizzo landslide. For this purpose, a 2D groundwater parametrical modeling was coupled with a slope stability analysis; the simulation was carried out by changing the values of the main hydrogeological parameters (recharge, groundwater supply level, etc.). The results enabled to connect the slope instability to some hydrogeological characteristics that are easy to survey and to monitor (e.g., rainfall, piezometrical level, and spring discharge), pointing out the hazard thresholds with regards to different triggering phenomena.     

Monitoring and evaluating the stability of soil slopes: A review on various available methods and feasibility of acoustic emission technique

Landslide is considered as one of the major natural hazards that cause enormous social and economic losses. Some techniques have been introduced to monitor the stability of slopes, such as the global positioning system (GPS), the geographic information system (GIS), aerial photography, and inclinometers. Each one of these offers advantages against others, but at the same time also possesses different limitations. Acoustic emission (AE) has recently been recognized as one of the feasible techniques to monitor soil slope. This article addresses issues involved in application of AE techniques for soil slope monitoring. If it is properly instrumented, AE could provide effective solutions for detecting early activities related to landslide development and giving early warning to such failures.     

2D Self-Potential tomographies for studying groundwater flows in the Varco d''Izzo landslide (Basilicata, southern Italy)

A geoelectrical monitoring activity has been carried out to improve the geological and hydrogeological knowledge about the Varco d'Izzo landslide (Potenza, Basilicata, Southern Apennine, Italy), an active rotational–translational slide evolved in earth-flow. In this work we have focused on the Self-Potential (SP) method by applying three different SP measuring techniques and combining modern technologies for data acquisition and new methods for tomographic inversion. A SP map and three static SP tomographies have been carried out to better analyse the groundwater circulation system and to better reconstruct the geometry of the landslide body. In the accumulation zone, which is the area more exposed to the geomorphological activity, a new SP measuring strategy has been applied. This strategy, based on time-continuous 2D SP tomographies, helps identify water flow changes in subsurface by studying the time series of SP tomographic images. The analysis of time-dependent changes of water infiltration in near surface is the key to better understand the hydrogeological processes underlying the ground instability phenomena. The time-lapse analysis of tomographic images has allowed us not only to investigate the correlation between the temporal changes of SP signals and rainfall events, but also to quantify the range of these changes. The modification of the distribution of the SP source accumulation zones is associated with the dynamics of the groundwater flows. These preliminary results allow us to consider the SP tomographic method as a tool for geophysical monitoring of landslide areas and encourage to develop new measuring systems for near-real time applications.     

Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications

Slope instability research and susceptibility mapping is a fundamental component of hazard management and an important basis for provision of measures aimed at decreasing the risk of living with landslides. On this basis, this paper presents the result of a comprehensive study on slope stability analyses and landslide susceptibility mapping carried out in part of Sado Island of Japan. Various types of landslides occurred in the island throughout history. Little is known about the triggering factors and severity of old landslides, but for many of the recent slope failures, the slope characteristics and stratigraphy are such that ground surfaces retain water perennially and landslides occur when additional moisture is induced during rainfall and snowmelt. A range of methods are available in literature for preparation of landslide susceptibility maps. In this study we used two methods namely, the analytical hierarchy process (AHP) and logistic regression, to produce and later compare two susceptibility maps. AHP is a semi-qualitative method, which involves a matrix-based pair-wise comparison of the contribution of different factors for landsliding. Logistic regression on the other hand promotes a multivariate statistical analysis with an objective to find the best-fitting model that describes the relationship between the presence or absence of landslides (dependent variable) and a set of causal factors (independent parameters). Elevation, lithology and slope gradient were casual factors in this study. The determinations of factor weights by AHP and logistic regression were preceded by the calculation of class weights (landslide densities) based on bivariate statistical analyses (BSA). The differences between the AHP derived susceptibility map and the logistic regression counterpart are relatively minor when broad-based classifications are considered. However, with an increase in the number of susceptibility classes, the logistic regression map gave more details but the one derived by AHP failed to do so. The reason is that the majority of pixels in the AHP map have high values, and an increase in the number of classes gives little change in the spatial distribution of susceptibility zones in the middle. To verify the practicality of the two susceptibility maps, both of them were compared with a landslide activity map containing 18 active landslide zones. The outcome was that the active landslide zones do not completely fit into the very high susceptibility class of both maps for various reasons. But 70% of these landslide zones fall into the high and very high susceptibility zones of the AHP map while this is 63% in the case of logistic regression. This indicates that despite the skewed distribution of susceptibility indices, the AHP map was better to capture the reality on the ground than the logistic regression equivalent.     

Regional Assessment of the Effects of Land-Use Change on Landslide Hazard By Means of Physically Based Modelling

Physically based models are capable of evaluating the effects of environmental changes through adaptations in their parameters. For landslide hazard zonation, this gives them an edge over traditional, statistically based techniques that require large datasets and often lack the objectivity to achieve the same purpose. Therefore, physical models can be valuable tools for hazard assessment and planning purposes.The usefulness of the model prognosis depends largely on the ability of the physical model to mimic the landscape system. This implies that the model should be calibrated and validated and that the imposed changes do not lead to a radical departure from the present situation.Under the recognition of these constraints, a physically based model has been applied to a 1.5 km² catchment in the Alcoy region (SE Spain) to evaluate the effects of land use change on landslide activity. The model couples a transient, distributed hydrological model with a probabilistic assessment of the slope stability. Thus, it is able to assess the spatial and temporal activity of slope instability. For the present situation, validation demonstrates that the probability of failure returns a conservative estimate of the spatial frequency of landsliding. The model has subsequently been applied to two hypothetical land use change scenarios that extrapolate present and likely trends. For these scenarios, the model results indicate a marginal decrease in the spatial frequency of landsliding (aerial extent of instability). However, the decrease in the temporal activity (is total duration of instability over a given period) is substantial under the altered land use conditions. The forecasted change in landslide activity not only affects the relative weight of slope processes in the region. It also has implications for the perceived hazard levels and the landslide hazard zonation of the area.     

Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy

This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models (conditional analysis and logistic regression), and neural network application, in order to better understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological, and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means of indirect-quantitative statistical methods and neural network software application. Experimental results from different methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic (ROC) curves analysis approach.     

Landslides in Sado Island of Japan: Part I. Case studies, monitoring techniques and environmental considerations

A sufficient knowledge on the kinematics and development of landslides helps to adopt proper measures that can be used to protect slopes and the environment in general. This can be achieved by adequate monitoring programs. This paper presents the findings of intensive monitoring activities carried out on Shiidomari and Katanoo landslides found in Sado Island of Japan. More than one year of observation of the two landslides allowed defining some peculiar futures of their kinematics and style of development. The problem of slope instability in the two areas is generally accredited to various factors. But, both landslides were triggered by heavy rainfalls and snowmelt. Because of the outline of the area and the presence of relict topographic features, the Shiidomari landslide is considered to be a large-scale reactivation of old slope failures. The Katanoo landslide is, however, a first-time case. Geophysical investigations and drilling activities in Shiidomari indicated the presence of two slip planes. The deepest (80–100 m) of these is controlled by existing lineaments. Monitoring data suggests that the body of the landslide has subsided as much as 1.16 m just below the main scarp, but a centimeter in the central region. The toe sector also experienced a significant amount of subsidence, but this was counter-balanced by an uplift on the opposite side of the landslide. Hence, the landslide seems not any more active along the deepest slip surface, although it may extend upward and define a series of shallow shear planes around the crown. In the case of Katanoo, the landform characteristics, differential weathering, the road cut and groundwater fluctuations appeared to contribute much to determine the exact location of the landslide. Extensional cracks that preceded the landslide can be related to heavy rainfalls and the cold and warm cycles thereafter. Subsurface investigations and monitoring works indicated that the landslide has two slide blocks with different slip planes. During the observation period, the upper part of the landslide responded more effectively to rainfall and snowmelt than the middle and lower sections. The corresponding movements, however, appeared to settle about three months after failure. There were also little strain transmissions in boreholes and no significant change in the characteristics of the landslide. The kinematics of deformation of many of the slopes in Sado Island resembles that of Shiidomari landslide. But mass movements along highways and mountain roads are usually similar to Katanoo. Landslides of the type like Shiidomari may not show sudden and drastic failures, but are usually long lasting and can reactivate repeatedly along new, shallow shear planes. Monitoring works and long-term supervisions in these types of landslides are useful to identify impending failures and take the right measures before they brought about large-scale destruction to the environment.     

A landslide database for Nicaragua: a tool for landslide-hazard management

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 Nicaragüense 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.     

Landslide hazard and risk mapping at catchment scale in the Arno River basin

We present the methodologies adopted and the outcomes obtained in the analysis of landslide risk in the basin of the Arno River (Central Italy) in the framework of a project sponsored by the Basin Authority of the Arno River, started in the year 2002 and completed at the beginning of 2005. In particular, a complete set of methods and applications for the assessment of landslide susceptibility and risk are described and discussed. A new landslide inventory of the whole area was realized, using conventional (aerial-photo interpretation and field surveys) and non-conventional methods (e.g. remote sensing techniques such as DInSAR and PS-InSAR). The great majority of the mapped mass movements are rotational slides (75%), solifluctions and other shallow slow movements (17%) and flows (5%), while soil slips, and other rapid landslides, seem less frequent everywhere within the basin. The relationships between landslide characteristics and environmental factors have been assessed through statistical analysis. As expected, the results show a strong control of land cover, lithology and morphology on landslide occurrence. The landslide frequency-size distribution shows a typical scaling behaviour already underlined in other landslide inventories worldwide. The assessment of landslide hazard in terms of probability of occurrence in a given time, based for mapped landslides on direct and indirect observations of the state of activity and recurrence time, has been extended to landslide-free areas through the application of statistical methods implemented in an artificial neural network (ANN). Unique conditions units (UCU) were defined by the map overlay of landslide preparatory factors (lithology, land cover, slope gradient, slope curvature and upslope contributing area) and afterwards used to construct a series of model vectors for the training and test of the ANN. Various different ANNs were selected throughout the basin, until each UCU was assigned a degree of membership to a susceptibility and a hazard class. Model validation confirms that prediction results are very good, with an average percentage of correctly recognized mass movements of about 85%. The analysis also revealed the existence of a large number of unmapped mass movements, thus contributing to the completeness of the final inventory. Temporal hazard was estimated via the translation of state of activity in recurrence time and hence probability of occurrence. The following intersection of hazard values with vulnerability and exposure figures, obtained by reclassification of digital vector mapping at 1:10,000 scale, lead to the definition of risk values for each terrain unit for different periods of time into the future. The final results of the research are now undergoing a process of integration and implementation within land planning and risk prevention policies and practices at local and national level.     

Proposal of a method to define areas of landslide hazard and application to an area of the Dolomites, Italy

A numerical–cartographical method has been developed to create landslide hazard maps. This method allows the assigning of a rating to the various parameters which contribute to landslides. The parameters considered are: (1) erodibility and degradability of the rocks and Quaternary deposits; (2) permeability of the ground to identify areas prone to hydraulic overpressure; (3) the geometric ratio between discontinuities and slope, and thickness of Quaternary deposits; (4) angle of the slopes; and (5) land use. A thematic map is constructed for each factor considered which defines different areas through ratings, after which all the thematic maps are overlaid and the ratings added up (or multiplied). The map which is thus obtained is reclassified in order to create the final map of landslide hazard. This method, which has already been tested in various areas, has produced excellent results in this case too, allowing a map to be constructed which corresponds to the actual instability problems.     

边坡位移演化的多尺度分析

通过相邻两个时间区间位移观测结果均值差分的积分描述和Harr小波基函数的小波变换方程的对比分析,得到了一个将不同时间尺度下边坡位移均值变化表示为相应尺度下小波变换系数的基本关系。根据这一关系,提出了可以采用小波变换方法确定两个相邻时间区间位移均值在不同时间尺度下变化规律的边坡位移演化的多尺度分析方法。针对卧龙寺新滑坡、三峡永久船闸边坡开挖和隔河岩水电站进水口边坡变形的观测结果,讨论了它们的位移演化多尺度特征。当边坡位移呈现较规则的变化趋势时,在一个尺度上就可以提取它们的时间演化特征。开挖剧烈扰动的影响可以通过给定时间尺度支撑区端点与位移突变点之间递增连的线进行近似。     

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.     

An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.     

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.     

Scale-invariance of soil moisture variability and its implications for the frequency-size distribution of landslides

Power spectral analyses of soil moisture variability are carried out from scales of 100 m to 10 km on the microwave remotely-sensed data from the Washita experimental watershed during 1992. The power spectrum S(k) has an approximate power-law dependence on wave number k with the exponent −1.8. This behavior is consistent with the behavior of a stochastic differential equation for soil moisture at a point, and it has important consequences for the frequency-size distribution of landslides. We present the cumulative frequency-size distributions of landslides induced by precipitation in Japan and Bolivia as well as landslides triggered by the 1994 Northridge, California earthquake. Large landslides in these regions, despite being triggered by different mechanisms, have a cumulative frequency-size distribution with a power-law dependence on area with an exponent ranging from −1.5 to −2. We use a soil moisture field with the above statistics in conjunction with a slope stability analysis to model the frequency-size distribution of landslides. In our model, landslides occur when a threshold shear stress dependent on cohesion, pore pressure, internal friction and slope angle is exceeded. This implies a threshold dependence on soil moisture and slope angle since cohesion, pore pressure and internal friction are primarily dependent on soil moisture. The cumulative frequency-size distribution of domains of shear stress greater than a threshold value with soil moisture modeled as above and topography modeled as a Brownian walk is a power-law function of area with an exponent of −1.8 for large landslide areas. This distribution is similar to that observed for landslides. The effect of strong ground motion from earthquakes lowers the shear stress necessary for failure, but does not change the frequency-size distribution of failed areas. This is consistent with observations. This work suggests that remote sensing of soil moisture can be of great importance in monitoring landslide hazards and proposes a specific quantitative model for landslide hazard assessment.     

Landslide hazard zonation by deterministic analysis (Veľká Čausa landslide area, Slovakia)

A practical application of a simple and economical solution to landslide hazard zonation based on slope stability analysis was carried out in the Veľká Čausa landslide, Horná Nitra region, central Slovakia. The region is prone to different types of slope deformation controlled by geological structure, physical and mechanical properties of materials, complicated hydrogeological setting, undulating morphology, and man-made influence. Taking into consideration the cause of the landslide, identified as groundwater change, two scenarios of landslide activity have been investigated. Scenario 1 considers the maximum groundwater level recorded from March 1995 to October 1998, corresponding to the period starting from the most recent landslide activity up to the end of remediation work. Scenario 2 considers the maximum groundwater level recorded from November 1998 to December 2004, after the remediation works, and corresponding to the actual situation of the landslide. It has been found from this study that slope angle has the highest influence on landslide instability in the Veľká Čausa landslide. Therefore, high resolution Digital Elevation Model (DEM) is essential for obtaining reasonable results. In addition, an appropriate selection of the model input parameters (e.g., shear strength) is very important. The validation between the calculated landslide hazard zonation map and results of monitoring survey were examined. The results show moderate to good agreement with the inclinometric and geodetic measurements. It was also verified that the most active part of the landslide is the north-western side.     

金沙江下虎跳滑石板滑坡破坏机理及稳定性分析

1996年10月28日发生在金沙江下虎跳峡口的滑石板滑坡,因堵塞金沙江、摧毁公路而引起高度重视。野外调查发现,滑石板滑坡是典型结构面控制的顺层基岩滑坡,层面和两组近于垂直层面的节理将边坡厚层灰岩切割成不同规模的菱形块体,长期的风化作用、重力、降雨、人工开挖公路等因素作用下,这些菱形块体从顺层稳定状态演化为稳定性较差的悬挂体,受河谷边坡斜向割切影响,表现为梯级叠次悬挂,最外边的悬挂体稳定性最差,最容易发生快速滑动。因此,该滑坡每次发生破坏滑动的体积不会很大,但是具有多次重复发生的特点,值得高度注意。     

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.     

攀钢石灰石矿H3滑坡研究

四川攀钢石灰石矿曾发生多次大规模滑坡,给矿山生产造成巨大损失。以其中规模较大的Ｈ３滑坡为研究对象,分析探讨该边坡的失稳原因,建立边坡失稳概念模型,并用离散单元法论证该边坡概念模型的合理性。     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.