Search

The international journal Landslides was initiated in April 2004. It is the core project (IPL-C100) of the International Programme on Landslides, a joint initiative of the International Consortium on Landslides and the United Nations and other global organizations. The aims of Landslides are to promote landslide sciences, technology, and capacity building and strengthen global cooperation for landslide risk reduction within the United Nations International Strategy for Disaster Reduction. This paper presents an analysis of the first 5 years of Landslides, the study methods employed, the types and major causes of landslides, the number of different contributors per country, and the “times cited” per issue and most frequently cited papers and briefly discusses some of these. Strategies for future development of the journal involve obtaining input and suggestion from researchers and readers worldwide. [ABSTRACT FROM AUTHOR]

Among the types of landslides that occur in Alpine areas, rockfalls are one of the most frequent and dangerous. Rockfalls affect every type of rock mass with greater or lesser incidence based on the intrinsic characteristics of rocks (structural setting, lithotype, geotechnical quality, weathering conditions, etc.) and climate features of geographic areas considered. In order to assess the hazard and subsequently the risk associated with these phenomena, it is first necessary to identify areas most likely to be affected. This paper presents a methodology applicable on a regional scale to identify the most susceptible Alpine areas to rockfalls. The methodology is based on statistical cross-analyses of environmental factors to identify the Alpine areas most susceptible to rockfalls. The predisposing factors considered to generate a rockfall susceptibility map are both qualitative (geological variables) and quantitative (morphometric, altimetric and climatic variables) and have been compared with a selection of historical data on rockfalls occurrence and distribution (consisting in 2800 events). As result, a classification of the Piemonte's Alpine territory identifying 5 classes characterized by a greater or lesser propensity to generate rockfalls has been obtained. The regional susceptibility map has been subsequently used as basis to identify road networks more exposed to rockfall hazard to obtain a preliminary risk analysis at regional scale. [ABSTRACT FROM AUTHOR]

Long-runout landslides can be extremely hazardous and unpredictable. The prediction of long-runout landslides is challenging because it is unclear what factors control their mobility, and it is unclear how to best measure their runout given that they traverse changing topography. In this paper, we document the development and statistical evaluation of a Landslide Runout Score (LRS) system to predict short, medium, and long runout using a new mobility measure, the unitless Runout Number L/A1/2 (where L is landslide length and A is landslide area). The Runout Number has previously been correlated to three geomorphological factors (planimetric curvature, sand content, and upslope contributing area normalized to landslide area), so the LRS uses these factors as inputs. These factors are readily calculated using geographic information systems (GIS) and publicly available data sources. The LRS system predicts runout categories (short, medium, and long) with one-vs.-all accuracies (i.e., percentage of correct predictions within a given class) of 75, 58, and 72%, respectively, for a total weighted accuracy of approximately 65%. The results of this work are summarized in a worksheet that can be used by geologists and engineers to develop preliminary predictions of landslide runout behavior, which can be incorporated into or used alongside regional-scale landslide hazard assessments. [ABSTRACT FROM AUTHOR]

Geomorphological analysis of landslide processes in mountainous terrains with difficult access has benefited from virtual representation of topography through the use of high-resolution digital elevation models (DEMs) generated by use of light detecting and ranging. Generic models of overlay and interpolation take advantage of the high-resolution DEMs for volume calculation. However, the advantages and limitations of the models at regional scale have received scant attention. These challenges are magnified in landslide hazard zonation mapping projects at state or national level, where the models need to be implemented for large datasets. To address the above deficiency, this paper presents a means to estimate landslide volume production and distribution by taking full advantage of LiDAR and to standardize landslide volume calculation in a geographic information system (GIS). We implemented two generic landslide volume models by using Python scripts; this is a systematic methodology for modeling volume of shallow and deep-seated landslides. The models were tested in real and theoretical conditions to highlight advantages and limitations. At the same time, we explored how the interpolation model is affected by local altimetric variation. The results show that one of the models can be used to make first-order interpretations regarding volume of eroded debris for landslide deposits at a local or national scale, while the other can help to assess the sequence of landslide activity. Theoretical evaluations show that local altimetric variation of < 1 m could lead to errors of almost 17%. The approach is explored with examples from Sumas Mountain in Whatcom County, Washington, USA. [ABSTRACT FROM AUTHOR]

The displacement of a large slow-moving landslide is accompanied by slope damage, such as fractures, tension cracks, and slope bulging. Studies of these features provide insight into the mechanisms responsible for the deformation. In this paper, we investigate slope damage at the Downie Slide, a very large landslide in British Columbia, Canada, that is slowly moving along two shear zones subparallel to the ground surface. Structural geology, and particularly the morphology of the lower shear zone, strongly controls the deformation and, in turn, the observed internal and surficial slope damage features. We use aerial and underground adit laser scanning and photogrammetry to characterize the geometry of the landslide. We subdivide the slide area into slope damage domains based on the distribution, size, and orientation of the slope damage features. We reconstruct the shape of the two failure surfaces by interpolating borehole inclinometer monitoring data and processing the 3D surfaces in a GIS environment to create aspect and slope maps. We observe a strong correlation between surficial slope damage features and changes in the dip and dip direction of the lower sliding surface. We further infer the presence of a multi-planar failure surface geometry and previously unrecognized structurally controlled damage zones. [ABSTRACT FROM AUTHOR]

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). Third, the spatial probability ( P) analysis was prepared by using the evidential belief function multiplied by the P 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. [ABSTRACT FROM AUTHOR]

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance. [ABSTRACT FROM AUTHOR]

An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. CHAID as a multivariate method has an automatic classification capacity to analyze large numbers of landslide conditioning factors. Moreover, it results two or more nodes for each independent variable, where every node contains numbers of presence or absence of landslides (dependent variable). Other DT methods such as Quick, Unbiased, Efficient Statistic Tree (QUEST) and Classification and Regression Trees (CRT) are not able to produce multi branches based tree. Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. In the first step, a landslide inventory map with 296 landslide locations were extracted from various sources over the Pohang-Kyeong Joo catchment (South Korea). Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. Thirteen landslide conditioning factors were used for the susceptibility modeling. Then, CHAID was applied and revealed that some conditioning factors such as altitude, soil drain, soil texture and TWI, as terminal nodes and reflected the best classification fit. Then, a proposed ensemble technique was applied and the interpretations of the coefficients showed that the relationship between the decision tree branch nodes distance from drain, soil drain, and TWI, respectively, leads to better consequences assessment of landslides in the current study area. The validation results showed that both success and prediction rates, 75 and 79 %, respectively. This study proved the efficiency and reliability of ensemble DT and LR model in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

A rockfall is a mass instability event frequently observed in road cuts, open pit mines and quarries, steep slopes and cliffs. After its detachment, the rock mass may disaggregate and break due to the impact with the ground surface, thus producing new rock fragments. The consideration of the fragmentation of the rockfall mass is critical for the calculation of the trajectories of the blocks and the impact energies and for the assessment of the potential damage and the design of protective structures. In this paper, we present RockGIS, a GIS-based tool that simulates stochastically the fragmentation of the rockfall, based on a lumped mass approach. In RockGIS, the fragmentation is triggered by the disaggregation of the detached rock mass through the pre-existing discontinuities just before the impact with the ground. An energy threshold is defined in order to determine whether the impacting blocks break or not. The distribution of the initial mass between a set of newly generated rock fragments is carried out stochastically following a power law. The trajectories of the new rock fragments are distributed within a cone. The fragmentation model has been calibrated and tested with a 10,000 m rockfall that took place in 2011 near Vilanova de Banat, Eastern Pyrenees, Spain. [ABSTRACT FROM AUTHOR]

Development of hilly areas requiring deforestation and increased human activities has raised many environmental issues in the world. This paper presents the results of investigation to assess the parameters that contribute to the risk to environment by reviewing available reports from Penang Island and surrounding areas. Nine parameters were identified that mostly contribute to the environmental risk. These are casualties of people, soil properties, earth coverage, soil grading characteristics, land use suitability, factor of safety, blasting area, distance between proposed structure to slope and geometry of slope. From these parameters, the rated risk approaches were proposed based on the prediction of occurrences of environmental issues. The rated risks were categorised from very high (5) to very low (1) with high, medium and low in between. The assessments were reviewed and evaluated based on rated risk for the chosen parameters. A map was developed identifying areas of environmental risk. The map can be used to quantify risk to the environment based on the parameters due to the proposed development. The overall result shows that areas PD3 of Tanjung Bungah and PD3, PD8 and PD9 of Paya Terubong have high-rated risk. No development area is classified as very high risk and very low-risk areas. These rating approaches can be used to develop potential risk zoning of proposed development areas. From this zoning guideline or standard, the viability of any proposed development can be measured. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Landslide susceptibility (LS) assessment by indirect approaches presents some limitations due to (1) the tendency to simplify the environmental factors (i.e., variables) and (2) the assumptions that landslides occur under the same combination of variables for a study site. Recently, some authors have discussed the interest to introduce expert knowledge in the indirect approaches in order to improve the quality of indirect LS maps. However, if the results are reliable, the procedures used seem fastidious and a very good knowledge of the study site is essential. The objectives of this paper are to discuss a methodology to introduce the expert knowledge in the indirect mapping process. After the definition of the expert rules associated to three landslide types, several indirect LS maps are produced by two indirect exploratory approaches, based on fuzzy set theory and on a modification of a bivariate method called expert weight of evidence. Then, the indirect LS maps are confronted to a landslide inventory and a LS map produced by a direct approach. The analyses indicate that the methodology used to introduce the expert rules in the mapping process increases the predictive power of indirect LS map. Finally, some indications about advantages and drawbacks of each approach are given to help the geoscientist to introduce his expert knowledge in the landslide susceptibility mapping process. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system (GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology, rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes were suggested by using the GIS technology. [ABSTRACT FROM AUTHOR]

Mailuu-Suu is a former uranium mining area in Kyrgyzstan (Central Asia) at the northern border of the Fergana Basin. This region is particularly prone to landslide hazards and, during the last 50 years, has experienced severe landslide disasters in the vicinity of numerous nuclear waste tailing dams. Due to its critical situation, the Mailuu-Suu region was and still is the target area for several risk assessment projects. This paper provides a brief review of previous studies, past landslide events and a discussion on possible future risk scenarios. Various aspects of landslide hazard and related impacts in the Mailuu-Suu Valley are analyzed in detail: landslide susceptibility, historical evolution of landslide activity, size-frequency relationship, river damming and flooding as well as impacts on inhabited areas and nuclear waste storage zones. The study was carried out with standard remote sensing tools for the processing of satellite imagery and the construction of digital elevation models (DEMs). The processed inputs were combined on a GIS platform with digital landslide distribution maps of 1962, 1977, and 2003, digitized geological and geographic maps, and information from landslide monitoring and geophysical investigation. As a result, various types of landslide susceptibility maps based on conditional analysis (CA) are presented as well as predictions of future landslide activity and related damming potential and their possible impact on the population. For some risk scenarios, remediation and prevention measures are suggested. [ABSTRACT FROM AUTHOR]

The Suusamyr region is located in the northern part of the Tien Shan Range in Central Asia. In 1992, this region was hit by the Ms = 7.3 Suusamyr earthquake triggering several large landslides along the Suusamyr Valley and on the southern slopes of the adjacent Suusamyr Range. One of these landslides had been investigated by geophysical and geotechnical methods in order to determine local trigger factors. The present paper focuses on the influence of geological and morphological factors upon landslide occurrence on a regional scale. The analysis is based on a digital data set including landslides triggered in 1992 and several older landslides as well as various types of digital elevation models (DEMs), ASTER image data, and geological and active fault maps. These data were combined to compute landslide susceptibility (LS) maps using statistical methods, Landslide Factor and Conditional Analyses (LFA, CA), as well as a geotechnical one, the Newmark's Method (NM). The landslide data set was also analyzed with respect to the size–frequency relationship. [ABSTRACT FROM AUTHOR]

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management. [ABSTRACT FROM AUTHOR]

It is well known that geological structure is one of the main predisposing factors that control landslide occurrence at regional scale. Along time, investigation of the role of structural and lithologic controls has raised the interest of specialists from various earth and planetary sciences who proposed different approaches that range from descriptive analysis to automatic detection. The aim of this study is to investigate the role of structural and lithological controls on landslide occurrence at regional scale. To achieve this, a collection of tools for calculating the TOBIA index and estimating the relationship between morphostructural and lithological conditions and landslide occurrences was developed using Python programming language. The relationship between geological structure and landslide occurrences was analyzed in three distinct landslide-prone sites in Romania, located in different geological and geomorphological contexts. The spatial distribution of the geological structure is modelled based on the TOBIA index (TOpographic/Bedding-plane Intersection Angle). The TOBIA index uses topographic slope, slope aspect, dip angle, and dip direction to divide the terrain into three categories named cataclinal, anaclinal, and orthoclinal, with each category having several subcategories. Additionally, by implementing the bivariate statistical analyses (Weight of Evidence), we investigated the relationship of landslide occurrences to the resulted morphostructural slopes, on the one hand, and to the lithological units, on the other hand. The output contrast values show an evident influence of the cataclinal slopes over landslide occurrences in homoclinal areas, with most of the scarps occurring at the contact between cataclinal and orthoclinal slopes. Fewer influences are found in areas where clay outcrops and where the geological structure has a much-diminished control over the presence of the landslides. These results contribute to a better understanding of the role of structural and lithologic controls on landslide occurrence at regional scale and furthermore could serve to a better assessment of landslide susceptibility. The results obtained on different geologic and geomorphic contexts in Romania suggest that this methodology can be applied successfully in other areas prone to landslides. For reproducibility purposes, the GIS set of tools was made available under the MIT license. [ABSTRACT FROM AUTHOR]

remote sensing and Geographic Information System-based study has been carried out for landslide susceptibility zonation in the Chamoli region, part of Garhwal Himalayas. Logistic regression has been applied to correlate the presence of landslides with independent physical factors including slope, aspect, relative relief, land use/cover, lithology, lineament, and drainage density. Coefficients of the categories of each factor have been obtained and used to assess the landslide probability value to ultimately categorize the area into various landslide susceptibility zones; very low, low, moderate, high, and very high. The results show that 71.13% of observed landslides fall in 21.96% of predicted very high and high susceptibility zone, which in fact should be the case. Furthermore, lineament first buffer category (0-500 m) and the east and south aspects are the most influential in causing landslides in the region. [ABSTRACT FROM AUTHOR]

Landslides constitute one of the most important natural hazards in Italy as they are widespread and result in considerable damage and fatalities every year. The Italian Landslide Inventory (IFFI) Project was launched in 1999 with the aim of identifying and mapping landslides over the entire Italian territory. The inventory currently holds over 480,000 landslides and has been available by means of Web services since 2005. The aim of this study is to define quality indices for evaluation of the homogeneity and completeness of the IFFI database. In order to estimate the completeness of the landslide attribute information, a heuristic approach has been used to assign weighting values to significant parameters selected from the landslide data sheet. The completeness and homogeneity of the landslide mapping has been evaluated by means of three different methods: an area-frequency distribution analysis; the proximity of the landslides surveyed to urban areas; variation of the landslide index within the same lithology. The quality indices have allowed identification of areas with a high level of completeness and critical areas in which the data collected have been underestimated or are not very accurate. The quality assessment of collected and stored data is essential in order to use the IFFI database for definition and implementation of landslide susceptibility models and for land use planning and management. [ABSTRACT FROM AUTHOR]

Statistical landslide susceptibility mapping is a topic in complete and constant evolution, especially since the introduction of machine learning (ML) methods. A new methodological approach is here presented, based on the ensemble of artificial neural network, generalized boosting model and maximum entropy ML algorithms. Such approach has been tested in the Monterosso al Mare area, Cinque Terre National Park (Northern Italy), severely hit by landslides in October 2011, following an extraordinary precipitation event, which caused extensive damage at this World Heritage site. Thirteen predisposing factors were selected and assessed according to the main characteristics of the territory and through variance inflation factor, whilst a database made of 260 landslides was adopted. Four different Ensemble techniques were applied, after the averaging of 300 stand-alone methods, each one providing validation scores such as ROC (receiver operating characteristics)/AUC (area under curve) and true skill statistics (TSS). A further model performance evaluation was achieved by assessing the uncertainty through the computation of the coefficient of variation (CV). Ensemble modelling thus showed improved reliability, testified by the higher scores, by the low values of CV and finally by a general consistency between the four Ensemble models adopted. Therefore, the improved reliability of Ensemble modelling confirms the efficacy and suitability of the proposed approach for decision-makers in land management at local and regional scales. [ABSTRACT FROM AUTHOR]

A complete landslide dam hazard management incorporates two assessment phases: the damming probability and the breach hazard. A prompt evaluation of the dam stability is crucial during the emergency to mitigate its consequences, but a reliable risk assessment can be realized only after the event has occurred, when the available time is very short. Therefore, it is necessary to develop tools able to help in mapping the spatial probability of damming over large areas for land-use planning, in order to better constrain consequence analysis and risk scenarios for setting up mitigation measures. In this work, a semi-automated GIS-based mapping methodology, based on a statistical correlation of morphometric parameters described by a morphological index, is proposed to spatially assess the likelihood of a river obstruction by landslide damming through two main mechanisms: the reactivation of existing landslides and the formation of new landslides. The two mapping methods (damming predisposition and damming probability) were used on a test area, the Arno River basin in Italy. The Eastern part of the basin resulted as the most susceptible to damming events in the whole basin. These are the highest mountain ridges in the basin (about 1600 m a.s.l.), characterized by calcareous, arenaceous, and marl lithology. The results are confirmed by the high concentration of the known historical landslide dams in the area according to existing inventories. [ABSTRACT FROM AUTHOR]

A landslide susceptibility map is a crucial tool for land-use spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method. [ABSTRACT FROM AUTHOR]

This study is aimed at developing rainfall intensity contour lines to illustrate upper and lower rainfall intensity limits that trigger landslides. The first phase began by developing precipitation empirical thresholds as follows: (i) the relationship between intensity (I) and accumulative rainfall (E) and date (I-date and E-date, respectively); (ii) the antecedent rainfall up to 3, 5, 10, 15, and 30 days prior to landslide occurrence; (iii) the relationship between the I and the duration (D); and (iv) the relationship between the cumulative rainfall event and the D (ID and ED, respectively). The data recorded by two rain gauges (Rize and Rize-Pazar) in the province of Rize in Northwest Turkey were used to analyze 24 previous landslide events during the period 1985 to 2006. The second phase began by developing surface interpolation maps after deriving threshold and assumption of normality, thereby producing contours of the rainfall intensity data of the rain gauges. All thresholds were verified by no-rainfall events, and the ID threshold successfully distinguished 97 and 95% (in Rize and Rize-Pazar, respectively) of false alarm days with the highest accuracy among the thresholds (antecedent rainfall days, I-date, and E-date thresholds). Thus, a descriptive analysis was conducted using the rainfall data of selected rain gauges to test the normality of rainfall between the selected rain gauges in the study area. The linear correlation coefficient was found at 0.75, and other tests verified the normality with optimistic results. A universal or collaborative kriging interpolation (UK) in a geographic information systems’ environment was used to estimate the spatial distribution of rainfall intensity because rainfall is controlled robustly by topography and other factors. This distribution corresponds to the landslide day event and hole-effect mathematical models that are used to forecast un-sampled values in the study area. Topographic elevation, slope, and normalized difference vegetation index (NDVI) maps were integrated into the landslide day events by using rainfall intensity. The UK map was validated by a cross-validation procedure using root-mean-square error. The highest interpolated surface map accuracy was reached without using an external drift (using rainfall intensity values only) and when using an NDVI as the external drift. The optimal model was selected by comparing the measured and predicted values at approximately 78% accuracy. Ultimately, the rainfall intensity contour lines were used to illustrate the upper and lower rainfall intensity limits that trigger landslides. This study used well-documented spatio-temporal data of landslide events and introduced a primary threshold that can be validated for any future event. [ABSTRACT FROM AUTHOR]

In the Vipava Valley (SW Slovenia), various types of mass movements occur in a geologically and geomorphologically diverse setting. These comprise various types of landslides, creep, and Quaternary slope deposits of carbonate blocks and recent scree deposits. A general geological setting is represented as Mesozoic carbonate overthrust on Paleogene flysch (alternations of mostly sandstones and marlstones), resulting in steep slopes and mass movements. Our study is based on the automatic classification of various litho-geomorphological units including slope deposits, alluvial deposits, steep carbonate cliffs, flysch, two carbonate plateaus, and Quaternary deposits, based on supervised Maximum Likelihood Classification. Several polygons were used for training in the broader valley area, and later, the method was applied to automatically classify the complete area into the abovementioned six units. For input layers, we used data for elevation, slope, terrain ruggedness index (TRI), and curvature. Results show that generally, the method is suitable for classification of the litho-geomorphological units including slope deposits. However, comparison with a more detailed map, comprising mapped various mass movements indicated that the method correctly predicts high Trnovo plateau carbonates, steep carbonate slopes, translational carbonate blocks, and fossil rock avalanche deposits, and alluvial deposits, but is not able to clearly distinguish between flysch and more recent slope deposits of gravel and breccia due to their similar elevation, TRI, and slope values. The Slano blato mudflow and Stogovce landslide are not recognized. Therefore, this automatic classification can be carefully used to create a guidance map of general occurrences of litho-geomorphological units including slope deposits before going to the field, with the aim of delineation of slope deposits so they can be further studied in detail later in the field. However, such a map cannot be used as a direct substitute map for the geological and geomorphological map obtained in the field due to impossibility in distinguishing among the units with the same properties (elevation, slope, and TRI values). [ABSTRACT FROM AUTHOR]

Mass movements in tropical Pacific small island developing states (SIDS) can be devastating although studies are relatively few and contributing environmental factors are not often investigated in detail. On 25 January 2012, following 3 days of heavy monsoonal rainfall (c. 550 mm) during a La Niña episode, more than 150 debris flows were triggered in the western part of the Ba river catchment of northwest Viti Levu island, Fiji. Reconnaissance field survey and geographical information system (GIS) analyses using high-resolution satellite imagery were carried out to investigate factors that may have led to the occurrence of the debris flows in the catchment. We evaluated the correlation between the density of mass movements (number of mass movements/km) and several continuous variables using data measured within the GIS. There was a weak but significant positive correlation between mass movement density and elevation ( r = 0.38, p value < 0.01), cyclonic precipitation ( r = 0.37, p value < 0.01) and stream density ( r = 0.31, p value < 0.01). Ninety-three percent of the mass movements occur within a plantation of Pinus caribaea (Caribbean pine) on slopes oriented mainly to the northeast and east on (trade) windward slopes and may be significant factors for their development. Although forests generally have a stabilizing effect on slopes, the plantation at Ba was a mature stand on its second plantation cycle and is a species that has a shallow rooting system making it more susceptible to failure. [ABSTRACT FROM AUTHOR]

The Selo landslide complex in the Vipava Valley (SW Slovenia) is a large fan-shaped sedimentary body that differs significantly from other slope deposits in the area in its exceptional size and considerable runout length. The landslide is predominantly composed of carbonate gravel deposited on a flysch paleo-relief. To determine the volume and geometry of the landslide and its potential source area, we integrated geological mapping, ground penetrating radar (GPR) and GIS techniques. The landslide deposits cover an area of about 10 km with an average thickness of 10 m (maximum thickness reaching 56 m) and a maximum length of 5500 m. The volume of carbonate gravel was estimated from geological cross sections and GPR profiles. The base and top surfaces were interpolated by inverse distance and kriging methods, for which the Cut/Fill method was used in ESRI ArcGIS to determine the original landslide volume before the erosion. The estimated original volume is 190 × 10 m. The recent volume after the erosion is 96 × 10 m. The calculated volume of the Selo landslide, angle of reach θ = 10° and H/L ratio of 0.18 are in accordance with data for landslides of a comparable size. The most reasonable explanation for the development of the Selo landslide complex is a slope collapse involving the breakdown of the rock mass and the development of a high-mobility rock avalanche. [ABSTRACT FROM AUTHOR]

The main objective of this study is to produce a landslide susceptibility map for the Lao Cai area (Vietnam) using a new hybrid intelligent method based on least squares support vector machines (LSSVM) and artificial bee colony (ABC) optimization, namely LSSVM-BC. LSSVM and ABC are state-of-the-art soft computing techniques that have been rarely utilized in landslide susceptibility assessment. LSSVM is adopted to develop landslide prediction model whereas ABC was used to optimize the prediction model by identifying an appropriate set of the LSSVM hyper-parameters. To establish the hybrid intelligent method, a GIS database with ten landslide-influencing factors and 340 landslide locations that occurred mainly during the last 20-years was constructed. These historical landslide locations were collected from the existing inventories that sourced from (i) five landslide projects carried out in this study areas before and (ii) interpretations of SPOT satellite images with resolution of 2.5 m. The study area was geographically split into two different parts, with landslides located in the first part was used for building models whereas the other landslides in the second part was used for the model validation. Performance of the LSSVM-BC model was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Result shows that the prediction power of the model is good with the area under the curve (AUC) = 0.900. Experiments have pointed out the prediction power of the LSSVM-BC is better than that obtained from the popular support vector machines. Therefore, the proposed model is a promising tool for spatial prediction of landslides at the study area. The landslide susceptibility map is useful for landuse planning for the Lao Cai area. [ABSTRACT FROM AUTHOR]

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength ( fs) and number of nearest neighbors ( k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC = 0.944) and validation dataset (AUC = 0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas. [ABSTRACT FROM AUTHOR]

This study proposes a probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by combining a transient infiltration flow model and Monte Carlo simulations. The spatiotemporal change in pore water pressure over time caused by rainfall infiltration is one of the most important factors causing landslides. Therefore, the transient infiltration hydrogeological model was adopted to estimate the pore water pressure within the hill slope and to analyze landslide susceptibility. In addition, because of the inherent uncertainty and variability caused by complex geological conditions and the limited number of available soil samples over a large area, this study utilized probabilistic analysis based on Monte Carlo simulations to account for the variability in the input parameters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. To evaluate its effectiveness, the proposed analysis method was applied to a study area that had experienced a large number of landslides in July 2006. For the susceptibility analysis, a spatial database of input parameters and a landslide inventory map were constructed in a GIS environment. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. In addition, the probabilistic method exhibited better performance than the deterministic alternative. Thus, analysis methods that account for uncertainties in input parameters are more appropriate for analysis of an extensive area, for which uncertainties may significantly affect the predictions because of the large area and limited data. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to define the main variables that contribute to the occurrence of landslides in Kimi, Euboea, Greece, and to produce a landslide susceptibility map using the weight of evidence method. For the developed model, a sensitivity analysis is carried out in order to understand the model's behavior when small changes are introduced in the weight value of the landslide-related variables. Landslide locations were identified from field surveys and interpretation of aerial photographs which resulted in the construction of an inventory map with 132 landslide events, while eight contributing variables were identified and exploited. All landslide-related variables were converted into a 5 × 5-m float-type raster file. These input-raster layers included a lithological unit layer, an elevation layer, a slope angle layer, a slope aspect layer, a distance from tectonic features layer, a distance from hydrographic network layer, a topographic wetness index layer, and a curvature layer. The validation of the developed model was achieved by using a subset of unprocessed landslide data, showing a satisfactory agreement between the expected and existing landslide susceptibility level, with the area under the predictive rate curve estimated to be 0.808. The area under the success rate curve was estimated to be 0.828 indicating a very high classification rate for existing landslide areas. According to the results of the sensitivity analysis, the lithological unit 'yellowish gray to white marls' was the most sensitive as it had the highest change in the relative frequency of observed landslides. The overall outcomes of the performed analysis provide crucial knowledge in successful land use planning and management practice and also in risk reduction projects. [ABSTRACT FROM AUTHOR]

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Rainfall-triggered shallow slope failures are very common in the western Southern Alps of New Zealand, causing widespread damage to property and infrastructure, injury and loss of life. This study develops a geographic information system (GIS)-based approach for shallow landslide/debris-flow susceptibility assessment. Since landslides are complex and their prediction involves many uncertainties, fuzzy logic is used to deal with uncertainties inherent in spatial analysis and limited knowledge on the relationship between conditioning factors and slope instability. A landslide inventory was compiled using data from existing catalogues, satellite imagery and field observations. Ten parameters were initially identified as the most important conditioning factors for rainfall-generated slope failures in the study area, and fuzzy memberships were established between each parameter and landslide occurrence based on both the landslide inventory and user-defined functions. Three output landslide susceptibility maps were developed and evaluated in a test area using an independent population of landslides. The models demonstrated satisfactory performance with area under the curve (AUC) varying from 0.708 to 0.727. Sensitivity analyses showed that a six-parameter model using slope angle, lithology, slope aspect, proximity to faults, soil induration, and proximity to drainage network had the highest predictive performance (AUC = 0.734). The runout path and distance of potential future landslides from the susceptible areas were also modelled based on a multiple flow direction algorithm and the topographic slope of existing debris-flow deposits. The final susceptibility map has the potential to inform regional-scale land-use planning and to prioritize areas where hazard mitigation measures are required. [ABSTRACT FROM AUTHOR]

In this work, a simple methodology is presented for processing high-resolution topographical data over wide areas. It is based on digital elevation model of differences (DEMoD) among high-resolution digital models (HRDEM) produced from light-detection and ranging (LiDAR) data. Because these qualitative approaches based on HRDEMs can be affected by errors related to misalignment between different passes of the airborne sensor and errors in classifying points, a simplified strategy was undertaken for their semi-automatic correction and supervision for analyzing geomorphological changes. Besides, it became possible to detect, delineate, and classify a total of 47 natural landslides and 50 slope-cut failures over an area of 234 km on the basis of the analysis of the LiDAR products (DEMs and DEMoD) and the orthophotography imagery inspection integrated in a geographical information system (GIS). Most of the displacements detected were probably generated during the winter of 2009-2010 when a new record of cumulative rainfall was reached. The displacement rate of these movements cannot be known with precision, but the minimum velocity that can be obtained is 0.3 m/year regarding the period between the two data acquisitions carried out in November 2008 and July 2010. On the other hand, a comparison was made of the existing susceptibility maps with respect to this new inventory, which indicated greater landslide frequency in areas of moderate susceptibility levels. The influence of treating inventories at different temporal scales is discussed. [ABSTRACT FROM AUTHOR]

The major scope of the study is the assessment of landslide susceptibility of Flysch areas including the Penninic Klippen in the Vienna Forest (Lower Austria) by means of Geographical Information System (GIS)-based modelling. A statistical/probabilistic method, referred to as Weights-of-Evidence (WofE), is applied in a GIS environment in order to derive quantitative spatial information on the predisposition to landslides. While previous research in this area concentrated on local geomorphological, pedological and slope stability analyses, the present study is carried out at a regional level. The results of the modelling emphasise the relevance of clay shale zones within the Flysch formations for the occurrence of landslides. Moreover, the distribution of mass movements is closely connected to the fault system and nappe boundaries. An increased frequency of landslides is observed in the proximity to drainage lines, which can change to torrential conditions after heavy rainfall. Furthermore, landslide susceptibility is enhanced on N-W facing slopes, which are exposed to the prevailing direction of wind and rainfall. Both of the latter geofactors indirectly show the major importance of the hydrological conditions, in particular, of precipitation and surface runoff, for the occurrence of mass movements in the study area. Model performance was checked with an independent validation set of landslides, which are not used in the model. An area of 15% of the susceptibility map, classified as highly susceptible, 'predicted' 40% of the landslides. [ABSTRACT FROM AUTHOR]

Although earthquakes are thought to be one of the factors responsible for the occurrence of landslides in Hokkaido, there exist no enough records which can allow correlating many of the old slope failures in the island with earthquakes. In the absence of these records, an attempt was done in this study to use the abundance, frequency, magnitude, depth, and distribution of historical earthquakes to deduce that many of the slope failures in the region were triggered by strong and continuous seismicity. The determination of the zones of influences of selected earthquakes using an existing empirical function has also supported this conclusion. Moreover, the use of a 10% probability of exceedance of earthquake intensity in 50 years, and the geological and slope maps has allowed preparing a landslide hazard map which explains the role of future earthquakes in the formation of slope failures. The result indicates a high probability of occurrences of landslides in the hilly regions of the southeastern part of Hokkaido due to expected strong seismicity and earthquake intensities in these areas. On the other hand, the low level of intensity in the north has given rise to low probability of landslide hazard. There are also places in the center of the island and high intensity regions in the east where the probability of landslide hazard was influenced by the contribution of the geological and slope maps. [ABSTRACT FROM AUTHOR]

We describe and test a method for identifying alluvial fans likely to be affected by debris flows. It is based on identifying catchment parameters by geographical information system interrogation of a digital elevation model, using the Melton ratio as the discriminating parameter. The method was calibrated using data from debris-flow-generating catchments in Coromandel and the adjacent Kaimai Ranges, North Island, NZ, and tested against data from the rest of New Zealand. The procedure is remarkably (but not completely) reliable for identifying debris-flow-capable catchments, and thus fans, across the wide range of climates and lithologies in New Zealand mountains. A case study illustrates the potential of the method for avoiding future hazards and emphasises the need for a precautionary approach when field investigations do not detect evidence for past debris flows. [ABSTRACT FROM AUTHOR]

In September 2004, rain from the remnants of Hurricanes Frances and Ivan triggered at least 155 landslides in the Blue Ridge Mountains of North Carolina. At least 33 debris flows occurred in Macon County, causing 5 deaths, destroying 16 homes, and damaging infrastructure. We mapped debris flows and debris deposits using a light-detecting and ranging digital elevation model, remote imagery and field studies integrated in a geographic information system. Evidence of past debris flows was found at all recent debris flow sites. Orographic rainfall enhancement along topographic escarpments influenced debris flow frequency at higher elevations. A possible trigger for the Wayah and fatal Peeks Creek debris flows was a spiral rain band within Ivan that moved across the area with short duration rainfall rates of 150–230 mm/h. Intersecting bedrock structures in polydeformed metamorphic rock influence the formation of catchments within structural–geomorphic domains where debris flows originate. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System (GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic regression models. The results of the analysis were verified using the landslide location data and compared with probability model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic regression (accuracy is 90.34%) model. [ABSTRACT FROM AUTHOR]

The purpose of the so-called IFFI project ( Inventario dei Fenomeni Franosi in Italia—Inventory of Landslides in Italy) and of many other related activities carried out by the Centro Regionale per le Ricerche Territoriali e Geologiche of ARPA Piemonte ( Agenzia Regionale per la Protezione Ambientale—Regional Agency for Environmental Protection), is to map all the existing landslides in Piemonte (including both results of monitoring data and available historical data). ARPA carried out new systematic surveys using airphoto interpretation and created a specific alphanumeric geological information systems (GIS)-based database to store and process all the collected data. In order to obtain proper landslide-hazard zoning, it is necessary to provide a landslide inventory and to define the relationship between landslides and geological setting. A landslide inventory represents a fundamental base of knowledge, is a very basic tool for land planning, and strongly helps the local authorities in their decision making. [ABSTRACT FROM AUTHOR]

On the basis of 1,834 landslide data for Hong Kong Island (HKI), landslide susceptibility maps were generated using logistic regression and GIS. Regional bias of the landslide inventory is examined by dividing the whole HKI into a southern and a northern region, separated by an east-west trending water divide. It was found that the susceptibility map of southern HKI generated by using the southern data differs significantly from that generated by using northern data, and similar conclusion can be drawn for the northern HKI. Therefore, a susceptibility map of HKI was established based on regional data analysis, and it was found to reflect closely the spatial distributions of historical landslides. Elevation appears to be the most dominant factor in controlling landslide occurrence, and this probably reflects that human developments are concentrated at certain elevations on the island. Classification plot, goodness of fit, and occurrence ratio were used to examine the reliability of the proposed susceptibility map. The size of landslide susceptible zones varies depending on the data sets used, thus this demonstrates that the historical landslide data may be biased and affected by human activities and geological settings on a regional basis. Therefore, indiscriminate use of regional-biased data should be avoided. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A spatial database of 791 landslides is analyzed using GIS to map landslide susceptibility in Tsugawa area of Agano River. Data from six landslide-controlling parameters namely lithology, slope gradient, aspect, elevation, and plan and profile curvatures are coded and inserted into the GIS. Later, an index-based approach is adopted both to put the various classes of the six parameters in order of their significance to the process of landsliding and weigh the impact of one parameter against another. Applying primary and secondary-level weights, a continuous scale of numerical indices is obtained with which the study area is divided into five classes of landslide susceptibility. Slope gradient and elevation are found to be important to delineate flatlands that will in no way be subjected to slope failure. The area which is at high scale of susceptibility lies on mid-slope mountains where relatively weak rocks such as sandstone, mudstone and tuff are outcropping as one unit. [ABSTRACT FROM AUTHOR]