Landslide Susceptibility and Hazard Modeling

An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16 pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making.

In this paper, we discuss the effect of non-homogeneity of the spontaneous polarization on electric susceptibility of ferroelectric films. Since the electric susceptibility represents response of the ferroelectrics to the given electric fields, hence determination of this parameter is very important. Using layer model, the spontaneous polarizations are calculated for each layer. Then these values are used in determining the electric susceptibility numerically. In this process, the effective medium theory is employed. It is found that the inhomogeneity of spontaneous polarization, especially near surface or bottom of the film, generates the small disturbance near resonance frequency of the ferroelectrics. The small dip appears in the susceptibility spectroscopy near the sharp dip which represents resonance. As the thickness of the film decreases, the small disturbance in susceptibility spectroscopy is increasingly visible. It means that the effect of inhomogeneity (surface effect) cannot be negligible in thin film. This result also shows the possibility to probe the surface effect by measuring reflectance while varying the frequency of incident wave.

During a severe rainstorm on 7 June 2008, over 2,000 landslides were recorded on Lantau Island, the largest island in Hong Kong. Numerous road links were severed and many landslides impinged on existing villages. As a result of this storm, and as part of the Hong Kong Government's recently commenced long term programme to assess the risk from natural terrain landslides to existing developments, an evaluation of potential landslide hazards affecting an area of some 18.5 km² in the western part of Lantau has been undertaken. The study reviewed both the landslide hazards, predominantly using an engineering geomorphological approach, and landslide consequences based on the impact to end users. A risk matrix approach was then applied in order to rank hillside catchments in terms of their overall risk and allow the prioritized selection of key study areas for further investigation and hazard mitigation.

Evaluating the performance of landslide susceptibility models is needed to ensure their reliable application to risk management and land-use planning. When results from multiple models are available, a comparison of their performance is necessary to select the model which performs better. In this paper, different techniques to evaluate model performance are discussed and tested using shallow landslide/debris-flow susceptibility models recently presented in the literature (Carrara, A., Crosta, G.B., Frattini, P., 2008. Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94 (3–4), 353–378). Moreover, an evaluation technique based on the minimization of costs that may arise from the adoption of the model as a land management regulatory tool, is presented. The results of the application show that simple statistics such as Accuracy, Threat score, Gilbert's skill score, Pierce's skill score, Heidke skill score, and Yule's Q are problematic as they need to split the classified objects into two classes (e.g., stable/unstable) by defining an a-priori value of cutoff susceptibility, which is often not trivial. ROC curves and Success-Rate curves are cutoff-independent and can be used to efficiently visualize and compare the performance of models, but do not explicitly include classification costs. In addition, Success-Rate curves, under certain conditions, can be misleading when applied to grid-cell models. Cost curves include costs and a-priori probabilities, and are suitable for landslide susceptibility model performance evaluation from a practical point of view.

Identification of landslides and production of landslide
susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning.
Accuracy of the landslide susceptibility maps is important for
reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of
Trabzon province (NE Turkey) using lithology, slope, land cover,
aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.

Se describe un procedimiento para evaluar el riesgo producido por la inestabilidad de laderas, en función de la amenaza y vulnerabilidad de los elementos expuestos a fenómenos de deslizamientos. Como ejemplo de aplicación, se presentan dos casos de estudio donde se evalúa el riesgo por deslizamientos de laderas en zonas urbanas de la ciudad de Tijuana, Baja California, México.

Nous avons développé des modèles pour évaluer les menaces naturelles principales auxquelles est exposé Delmas 32. La menace sismique, la menace d’inondation et la menace d’instabilité des pentes ont été retenues comme celles qui pourraient affecter le plus le quartier, surtout en tenant compte des caractéristiques du terrain et des conditions de vulnérabilité présentes. En apparence et suivant les donnés disponibles, la liquéfaction des sols sableux ne parait pas plausible de se matérialiser de manière importante. Tous nos commentaires et déductions sur l’instabilité des pentes sont générés à partir d’observations géologiques-géotechniques en surface. Il faudra les vérifier au moyen d’études spécifiques de terrain en profondeur (i.e. sondages carottées, SPT, résistivité électrique, sismique de réfraction, essais de laboratoire, calculs, etc.). De ceci et à partir de l’observation des affleurements dans les berges des ravines, nous avons constaté que la verticalité de leur falaises est directement liée au degré élevé de résistance au cisaillement des conglomérats qui les constituent. L’absence de nappe phréatique dans la couche supérieure des conglomérats favorise le maintien des pentes. Par contre, les couches d’argilite bien que de faible plasticité et relativement peu sensibles à l’eau et donc que leur résistance ne soit pas susceptible de subir de fortes chutes dues à la saturation. Cependant, elles sont fissiles et après plusieurs cycles d’humidification-dessiccation, elles deviennent susceptibles à l’érosion. Une condition critique à considérer est le cas de la zone affecté par le macro-glissement de terrain qui s’est produit en 1989 au sud du quartier de Delmas 32 (voir chapitre 3.2.2) et en partie causé par le mouvement d’un remblai mal compacté et qui aujourd’hui se trouve très densément peuple. Il existe une couche d’argile riche en montmorillonite, qui, lorsque saturée par une nappe phréatique pérenne se caractérise par une faible résistance au cisaillement. Celle-ci a été vraisemblablement à l’origine du macro-glissement de Delmas 32 de 1989. À cause de l’ampleur et de la forte densité des constructions actuelles, il n’est pas possible d’évaluer le degré d’activité, le taux de mouvement ni la possibilité d’une réactivation soudaine de ce mouvement de terrain. Le seul moyen de diagnostiquer la probabilité de reprise des mouvements serait issu d’une campagne d’études géotechniques, hydrogéologiques et géophysiques permettant de modéliser la masse glissante et ses propriétés mécaniques et hydrauliques. Ce macro-glissement n’est pas analysable avec la méthodologie Mora-Vahrson, qui vise plutôt à la potentialité du développement de glissements de plus petite échelle. Les analyses de stabilité géotechnique des pentes réalisées confirment les observations de terrain, à l’effet que les pentes sub-verticales des ravines sont assez stables à court terme, pourvu que l’activité humaine n’engendre pas des changements radicaux dans la condition du terrain. Cependant, les écoulements torrentiels à haut débit d’énergie dans les ravines, continueront à se présenter chaque fois que les pluies dépasseront une cinquantaine de millimètres par heure. Dans ces conditions, il faudrait envisager que les talus des ravines seront continuellement minés et à long terme ils peuvent devenir instables si aucune protection hydraulique adéquate contre l’érosion n’est assurée: d’une part au pied par le courant des rivières et d’autre part sur les crêtes contre le ruissellement de surface, au moyen de fossés de drainage et d’assainissement et en réparant les fuites des tuyauteries d’aqueducs pour éviter des écoulement qui se jettent sur les pentes et l’humidifient les terrains. Les méthodes de prévention et mitigation qui s’imposent dans ces zones exposées consistent essentiellement à améliorer le drainage pour éviter leur infiltration dans le sol: i) drainage des eaux de pluie au moyen de fossés d’assainissement pour éviter les stagnations; ii) canalisation des eaux usées qui sont actuellement rejetées directement sur les talus; iii) drainage souterrain en cas de présence de nappes phréatiques. Pour vérifier la stabilité générale des pentes, à long terme il faudrait réaliser, pour chaque endroit en particulier, des analyses de stabilité spécifiques. La vérification des conditions de stabilité et ses facteurs de sécurité peuvent être réalisés par des exercices de retro-calcul et de sensibilité paramétrique au moyen des logiciels tels que STABL®, SLOPE/W 2007®, ou autres. Il est clair que la fiabilité des résultats de ces calculs sera fonction de la qualité de la prospection géotechnique respective (reconnaissances de terrain, sondages, analyses de laboratoire, etc.). Les études hydrologiques et hydraulique menés jusqu’à présent ont permis de produire des scénarios et modélisations déterministes des zones inondables, au moyen des logiciels HECRAS et HECHMS. Il a été possible aussi de réaliser des zonages préliminaires de la menace d’inondation à Delmas 32. Dans le cas de la menace d’inondation, il est à considérer le fait que l’abondance et le manque de contrôle des déchets solides, mise à part les considérations environnementales évidentes et graves, constitue un problème majeur de multiplication et d’aggravation de la menace d’inondation, surtout à cause de leur influence dans les systèmes de drainage en général.
Dans les étapes futures d’étude, il faudra considérer trois scénarios d’analyse supplémentaires: i) Situation actuelle avec blocage par des déchets solides dans le système de drainage primaire et sous les ponts; ii) Situation avec les structures actuelles, sans blocage par des déchets solides; iii) Situation avec les structures actuelles redimensionnées et sans déchets solides. Ces scénarios doivent s’accompagner de guides pour la conception des mesures structurelles et non-structurelles, la relocalisation des bâtiments qui se trouvent dans les zones inondables et le redimensionnement des structures hydrauliques à capacité hydraulique insuffisante. Il faut aussi envisager d’interdire la réoccupation dans des zones inondables, gérer les déchets solides et d’implanter des programmes d’éducation et de gestion du risque pour diminuer la vulnérabilité face aux inondations. Par superposition des informations correspondantes à l’exposition des enjeux aux menaces naturelles, il a été possible de construire des cartes préliminaires où l’on peut identifier les niveaux combinés des menaces de glissement et d’inondation. Par convention, nous avons établit les indicatifs suivants sur nos fichiers SIG:
• Glissement = G (identifié par la méthode Mora-Vahrson)
• Inondation = I (suivant les modèles HEC)

Integrated geological and geophysical studies to investigate the landslide zones and study its impact: a case along the road section within Kurar Kebele, Dejen woreda, Central Ethiopia A Thesis Submitted to School of Graduate Studies of Addis Ababa University In partial fulfilment of the requirements for the Degree of Masters of Sciences (Environmental Geology and Geo-hazard)

We present a general framework for probabilistic landslide hazard analysis. With respect to other quantitative hazard assessment approaches, this probabilistic landslide hazard analysis has the advantage to provide hazard curves and maps, and to be applicable to all typologies of landslides, if necessary accounting for both their onset and transit probability. The method quantifies, for a given slope location, the exceedance probability of being affected by a landslide with a specific local intensity within a reference time interval, i.e. the hazard curve, under the common assumption that landslides behave as a Poisson process. Hazard maps are calculated, reducing the hazard curve to single values by choosing a fixed probability of exceedance following standards or regulation requirements. The method is based on the assessment of a landslide onset frequency, a runout frequency for long-runout landslides, and the local definition of landslide intensity, which can be expressed through different parameters, according to landslide typology. For long runout landslides, the runout and spatially-varying intensity and uncertainty are considered. Hazard curves and maps play a fundamental role in the design and dimensioning of mitigation structures, in land planning and in the definition of risk and hazard management policies. Starting from the general framework, we apply the methodology for rockfall hazard analysis, and we test it in an area affected by the Christchurch 2011 earthquake, New Zealand, which triggered a large number of rockfalls, killing five people.

Landslides are one of the most widespread natural hazards that cause damage to both property and life every year, and therefore, the spatial distribution of the landslide susceptibility is necessary for planning future developmental activities. In this paper the artificial neural network (ANN) technique is tested for developing a landslide susceptibility map in Turbolo River catchment, North Calabria, South Italy. Landslides were mapped through air-photo interpretation and field surveys, by identifying both the landslide depletion zones (DZs) and accumulation zones (AZs); and relevant geo-environmental thematic layers pertaining to landslide predisposing factors were generated using air-photo interpretation, field surveys and Geographic Information System (GIS) tools. Ten predisposing factors were related to the occurrence of landslide: lithology, faults, land use, drainage network, and a series of topographic factors: elevation, slope, aspect, plan curvature, to-pographic wetness index (TWI) and stream power index (SPI). In order to evaluate and validate landslide susceptibility, the DZs were divided in two groups using a random partition strategy. The first group (training set) was used to prepare the susceptibility map, employing a back-propagation learning algorithm in the Idrisi Taiga software. The second group (testing set) was used to validate the landslide susceptibility model, using the confusion matrix and the receiver operating characteristic (ROC) curve. The susceptibility map was classified into five susceptibility classes: very low, low, moderate, high, and very high. About 46% of the study area falls in high to very high susceptible classes and most of the DZs mapped (87.3%) occur in the same classes. The validation results showed satisfactory agreement between the susceptibility map and the DZs locations; over 85% of the DZs of the validation set are correctly classified falling in high and very high susceptibility areas. Also, the ROC curve had shown an area under curve (AUC) value of 0.90 which demonstrates the robustness and good reliability of the landslide susceptibility model. According to these results, we conclude that the map produced by the artificial neural network is reliable and the methodology applied in the study produced high performance, and satisfactory results, which may be useful for land planning policy.

Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.

This study presenteds prediction capability of three methods namely including Frequency Ratio (FR), Fuzzy Gamma (FG), and Landslide Index method (LIM) to produce of landslide prone areas in Sari-Kiasar watershed, Mazandaran Province of, Iran. In the first step, 105 landslides locations were was selected and they randomly divided into two groups of 75% (78 locations) and 25% (27 locations) as a training and testing dataset. Then, seventeen landslide conditioning factors including land use/land cover (LULC), Differential Vegetation Index (DVI), lithology and distance from faults, elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature, distance from drainage, rainfall, Stream Power Index (SPI), Sediment Transport Index (STI) and temperature, and distance from road, density of settlement were considered to the proposed modelling approach. Finally, by applying training dataset, three landslide susceptibility maps were constructed by using FR, FG, and LIM methods. The Pprediction capability of the performed model was evaluated by the Area Under the Receiver Operating Curve (ROC) or (AUC) for both of training (success rate) and testing (prediction rate) dataset. The results showed that the AUC for success rate for FR, FG and LIM models were 82.04%, 81.08% and 73.61% and for prediction rate were 82.72, 79.09% and 65.45%, respectively. The results showed that the FR model has a higher prediction accuracy than theof FG and LIM methods. This study revealed that the most important factors in landslide occurrence were are rainfall, slope, and vegetation. The Rresult of the present study can be possibly useful for the land use planning and watershed management.

Many landslide conditioning factors have been considered in the literature for landslide susceptibility mapping,
but it is not certain which factors produce the best result for an area under analysis. With the availability of increasing
number of landslide conditioning factors, finding the best combination of factors has become an important
research issue. In this study, genetic algorithms (GAs) were applied to find the best factor combination
among 16 factors available for the study area, Macka District of Trabzon, Turkey. Performances of the models including
4 to 15 factorswere evaluated using logistic regression to investigate the effect of varying number of factors
and the most effective factors were determined. Results showed that prediction accuracy of the models
constructed with GA-selected factors increased to a certain level (up to 8 factors) and then showed a stable
trend producing statistically similar results. In order to show the robustness of the GA algorithm, prediction performances
of the models constructed with 4 to 8 factors determined by the GA were compared with those of
models constructed with factor combinations applied in the literature. While slope, lithology and distance to
drainage were found to be the most effective factors, soil depth, slope length and profile curvature were found
to be the least effective ones. It was also found that the models determined by the GA generally produced better
results than user's models. Also, the goodness of the GA-based modelswas confirmed by success rate curve analysis
and McNemar's test. In summary, results revealed the robustness of the GAwhen searching the optimal landslide
conditioning factors among a large number of factors.

Currently, regional coseismic landslide hazard analyses require comprehensive historical landslide
inventories as well as detailed geotechnical data. Consequently, such analyses have not been possible where
these data are not available. A newapproach is proposed herein to assess coseismic landslide hazard at regional
scale for specific earthquake scenarios in areas without historical landslide inventories. The proposed model
employs fuzzy logic and geographic information systems to establish relationships between causative factors
and coseismic slope failures in regions with well-documented and substantially complete coseismic landslide
inventories. These relationships are then utilized to estimate the relative probability of landslide occurrence
in regions with neither historical landslide inventories nor detailed geotechnical data. Statistical analyses
of inventories from the 1994 Northridge and 2008 Wenchuan earthquakes reveal that shaking intensity,
topography, and distance from active faults and streams are the main controls on the spatial distribution of
coseismic landslides. Average fuzzy memberships for each factor are developed and aggregated to model the
relative coseismic landslide hazard for both earthquakes. The predictive capabilities of the models are assessed
and show good-to-excellent model performance for both events. These memberships are then applied to the
1999 Chi-Chi earthquake, using only a digital elevation model, active faultmap, and isoseismal data, replicating
prediction of a future event in a region lacking historic inventories and/or geotechnical data. This similarly
results in excellent model performance, demonstrating the model’s predictive potential and confirming it can
be meaningfully applied in regions where previous methods could not. For such regions, this method may
enable a greater ability to analyze coseismic landslide hazard from specific earthquake scenarios, allowing for
mitigation measures and emergency response plans to be better informed of earthquake-related hazards.

The principal aim of this study is to assess the landslide susceptibility in the Chuetsu area, Niigata Prefecture, Central Japan, using a certainty factor model in a GIS environment. The landslide inventory data used in this study were obtained from the National Research Institute for Earth Science and Disaster Prevention (NIED). The data were divided into two groups: one for training the model and the other for its validation. Seven relative factors, elevation, slope angle, slope aspect, density of geological boundary, density of drainage network, plan curvature, and lithology were utilized for this susceptibility analysis. Based on the aforementioned correlative factors, a landslide susceptibility map was produced and then verified using receiver operating characteristics (ROC). The value of area under the ROC curve (AUC) of the constructed CF model is 0.82. A model with such a high AUC value is considered good and therefore acceptable in predicting landslides. The landslide susceptibility map prepared in this study can hence be used to mitigate risks associated with landslides in the study area.

Landslide susceptibility maps are valuable sources for disaster mitigation works and future investments of local authorities in unstable hazard-prone areas. However, there are limitations and uncertainties inherent in landslide susceptibility assessment. For this purpose, many methods have been suggested and applied in the literature, which are generally categorized as bivariate and multivariate. Here, in this paper, the most popular and widely used multivariate [support vector regression (SVR), logistic regression (LR) and decision tree (DT)] and bivariate methods [frequency ratio (FR), weight of evidence (WOE) and statistical index (SI)] were compared with respect to their performances in landslide susceptibility modeling problem. Duzkoy district of Trabzon Province was selected due to its unique topographical and lithological characteristics, magnifying shallow landslide risk potential. Slope, lithology, land cover, aspect, normalized difference vegetation index, soil thickness, drainage density, topographical wetness index and elevation were employed as landslide occurrence factors. Accuracy measures based on confusion matrix (i.e., overall accuracy and Kappa coefficient) and receiver operating characteristic (ROC) curve were employed to compare the performances of the methods. Furthermore, McNemar's test was employed to analyze the statistical significance of differences in method performances. The results indicated that multivariate approaches (i.e., SVR, LR and DT) outperformed the bivariate methods (i.e., FR, SI and WOE) by about 13 %. Within the multivariate approaches, SVR method performed the best with the highest accuracy, while FR method was the most effective and accurate bivariate method. Interpretation of AUC values and the McNemar's statistical test results revealed that the SVR method was superior in modeling landslide susceptibility compared with the other multivariate and bivariate methods.

The ''Costa Viola'' mountain ridge (southern Calabria), in the sector between Bagnara Calabra and Scilla, is particularly exposed to geo-hydrological risk conditions. The study area has repeatedly been affected by slope instability events in the last decades, mainly related to debris slides, rock falls and debris flows. These types of slope movements are among the most destructive and dangerous for people and infrastructures, and are characterized by abrupt onset and extremely rapid movements. Susceptibility evaluations to shallow landslides have been performed by only focusing on source activation. A logistic regression approach has been applied to estimating the presence/absence of sources in terms of probability, on the basis of linear statistical relationships with a set of territorial variables. An inventory map of 181 sources, obtained from interpretation of air photographs taken in 1954-1955, has been used as training set, and another map of 81 sources, extracted from 1990 to 1991 photographs, has been adopted for validation purposes. An initial set of 12 territorial variables (i.e. lithology, land use, soil sand percentage, elevation, slope angle, aspect, across-slope and down-slope curvatures, topographic wetness index, distance to road, distance to fault and index of daily rainfall) has been considered. The adopted regression procedure consists of the following steps: (1) parameterization of the independent variables, (2) sampling, (3) calibration, (4) application and (5) evaluation of the forecasting capability. The ''best set'' of variables could be identified by iteratively excluding one variable at a time, and comparing the ROC results. Through a sensitivity analysis, the role of the considered factors in predisposing shallow slope failures in the study area has been evaluated. The results obtained for the Costa Viola mountain ridge can be considered acceptable, as 98.1 % of the cells are correctly classified. According to the susceptibility map, the village of Scilla and its surroundings fall in the highest susceptibility class.

Identification of landslide prone areas and production of accurate landslide susceptibility zonation maps have been crucial topics for hazard management studies. Since the prediction of susceptibility is one of the main processing steps in landslide susceptibility analysis, selection of a suitable prediction method plays an important role in the success of the susceptibility zonation process. Although simple statistical algorithms (e.g. logistic regression) have been widely used in the literature, the use of advanced non-parametric algorithms in landslide susceptibility zonation has recently become an active research topic. The main purpose of this study is to investigate the possible application of kernel-based Gaussian process regression (GPR) and support vector regression (SVR) for producing landslide susceptibility map of Tonya district of Trabzon, Turkey. Results of these two regression methods were compared with logistic regression (LR) method that is regarded as a benchmark method. Results showed that while kernel-based GPR and SVR methods generally produced similar results (90.46% and 90.37%, respectively), they outperformed the conventional LR method by about 18%. While confirming the superiority of the GPR method, statistical tests based on ROC statistics, success rate and prediction rate curves revealed the significant improvement in susceptibility map accuracy by applying kernel-based GPR and SVR methods.

The objectives of this research was to observe landslide susceptibility zonation at Secang Watershed and evaluate the accuracy of SINMAP model performance. The applied methodology that used in this research were field survey method, qualitative analysis method, quantitative analysis method and comparative analysis. Survey method were consists of observation, collecting actual landslide data, and soil sampling (undisturbed sample). Qualitative and quantitative analysis method were employed to measure variables input of SINMAP. Comparative method consists of validation and evaluation to modeling result. The accuration of SINMAP result was validated using success rate. The result shows that Secang Watershed were dominated by medium-very high susceptibility class. Very high susceptibility class covered an area about 5,8 km 2 (36,46 %). High susceptibility class covered an area about 7,21 km 2 (34,8%). Medium susceptibility class covered an area about 4,45 km 2 (21,29 %). The rest of 12,73% Secang Watershed area was classifed as safe area. Secang Watershed morphology that dominated by hills with steep slope and high rainfall was the main factor lead to high susceptibility to landslide. Validation of the model using success rate showed that SINMAP can predict landslide susceptibility class with value up to 79 % and almost all of landslide inventory was at medium-very high landslide susceptibility class.

Los deslizamientos son eventos que ocurren comúnmente en la mayoría de las vías del Ecuador, ya sea por los factores naturales del relieve, o por causas antrópicas. La vía Alóag-Santo Domingo es una de las más transitadas en el país, ya que por esta arteria vial se conecta la Sierra con la Costa; a pesar de su importancia interregional, ha sido históricamente afectada por deslizamientos con grandes pérdidas materiales y humanas, y sin llevar a cabo estudios que coadyuven su prevención. El objetivo de estudio fue determinar la susceptibilidad a deslizamientos en este importante eje vial, mediante Lógica Difusa como una propuesta técnica para la zonificación e identificación de puntos críticos de este peligro geológico que ayude en la planificación y toma de decisiones. Se utilizó siete variables: cercanía a ríos, vías, fallas geológicas; cobertura vegetal; tipo de roca; precipitación y pendiente, las cuales fueron normalizadas a través de una función de membresía según su relación con el evento analizado. Dos modelos fueron obtenidos, el primero resultante de un promedio difuso, y el segundo de una mixtura entre Lógica Difusa, combinación lineal ponderada y matriz de Saaty. El ajuste alcanzado fue 87.7% y 88.7% para el primer y segundo modelo respectivamente. Del modelamiento más preciso se determinó que el pueblo El Paraíso y el sector de La Unión del Toachi están en zona crítica, mientras que otros asentamientos se encuentran en zona alta de susceptibilidad. El modelo fue comparado con un registro de deslizamientos, de los cuales en su mayoría coinciden con las zonas críticas (75%) identificadas mediante Fuzzy, lo que respalda una correcta modelación realizada. Con este estudio, se ofrece una forma económica, precisa e integral de evaluar dicha amenaza que puede servir como insumo dentro del ordenamiento territorial y la gestión de riesgos.

In countries that show spatial variety over short distances such as Turkey, wrong-unconscious land use practices increase the number and impact area of disasters which cause life and property losses. Landslide is one of these disasters. In this study, we produced base maps for the Province of Kilis using GIS and remote sensing methodologies. Base maps in question were created taking parameters effective in the event of landslide into account. The research results showed that 14% of the Province of Kilis, in the west and northwestern of it in particular, had very high-high landslide risk. In this context, local administrators should consider, during land use planning, these sites where the landslide risk is high-very high. Otherwise, possible landslides may cause losses of life and property. In addition, the foundation of “the Natural Disaster Planning Center of the Province of Kilis” would be a positive step.
Keywords: GIS, Natural Disaster Planning, Landslide Susceptibility Classes, Kilis, Remote Sensing.

KİLİS İLİ’NDE HEYELAN RİSKİ YÜKSEK ALANLARIN ÇOK KRİTERLİ ANALİZ METODU İLE BELİRLENMESİ
Öz
Türkiye gibi kısa mesafelerde mekânsal açıdan çeşitlilik gösteren ülkelerde yanlış ve bilinçsiz arazi kullanım uygulamaları can ve mal kaybına neden olan afetlerin sayısını ve etki alanını artırmaktadır. Bu afetlerden birisi de heyelandır. Herhangi bir alanda heyelan duyarlılık sınıflarını belirlenmesi oldukça önemlidir. Bu çerçevede çalışmamızda CBS (Coğrafi Bilgi Sistemleri) ve uzaktan algılama metodolojileri kullanılarak Kilis İline ait altlık haritalar üretilmiştir. Söz konusu altlık haritalar, heyelan olayında etkili olan parametreler göz önünde bulundurularak oluşturulmuştur. Araştırma sonuçları Kilis İli’nin % 14’lük bir kısmında özellikle batısı ve kuzeybatısında heyelan riskinin çok yüksek ve yüksek olduğunu göstermektedir. Bu çerçevede yerel yöneticilerin arazi kullanım (yerleşim, tarım vb.) planlaması yaparken heyelan riskinin çok yüksek ve yüksek olduğu bu sahaları göz önünde bulundurmaları gerekmektedir. Aksi takdirde yaşanacak heyelanlar, can ve mal kaybına sebep olabilecektir. Bunlar yanında interdisipliner bir perspektife sahip “Kilis İli Doğal Afet Planlama Merkezi” kurulması olumlu bir adım olacaktır.
Anahtar kelimeler: CBS, Doğal Afet Planlaması, Heyelan Duyarlılık Sınıfları, Kilis, Uzaktan Algılama.

Landslide studies are commonly guided by ground knowledge and field measurements of rock strength and slope failure criteria. With increasing sophistication of GIS-based statistical methods, however, landslide susceptibility studies benefit from the integration of data collected from various sources and methods at different scales. This study presents a logistic regression method for landslide susceptibility mapping and verifies the result by comparing it with the geotechnical-based slope stability probability classification (SSPC) methodology. The study was carried out in a landslide-prone national highway road section in the northern Himalayas, India. Logistic regression model performance was assessed by the receiver operator characteristics (ROC) curve, showing an area under the curve equal to 0.83. Field validation of the SSPC results showed a correspondence of 72% between the high and very high susceptibility classes with present landslide occurrences. A spatial comparison of the two susceptibility maps revealed the significance of the geotechnical-based SSPC method as 90% of the area classified as high and very high susceptible zones by the logistic regression method corresponds to the high and very high class in the SSPC method. On the other hand, only 34% of the area classified as high and very high by the SSPC method falls in the high and very high classes of the logistic regression method. The underestimation by the logistic regression method can be attributed to the generalisation made by the statistical methods, so that a number of slopes existing in critical equilibrium condition might not be classified as high or very high susceptible zones.

Information regarding the shape and depth of a landslide sliding surface (LSS) is fundamental for the estimation of the volume of the unstable masses, which in turn is of primary importance for the assessment of landslide magnitude and risk scenarios as well as in refining stability analyses. To assess an LSS is not an easy task and is generally time-consuming and expensive. In this work, a method existing in the literature, based on the inclination of movement vectors along a cross-section to estimate the depth and geometry LSSs, is used for the first time while exploiting satellite interferometric data. Given the advent of satellite interferometric data and the related increasing availability of spatially dense and accurate measurements, we test the effectiveness of this method-here named the vector inclination method (VIM)-to four case landslides located in Italy characterized by different types of movement, kinematics and volume. Geotechnical and geophysical information of the LSS is used to validate the method. Our results show that each of the presented cases provides useful insight into the validity of VIM using satellite interferometric data. The main advantages of VIM applied to satellite interferometry are that it enables estimation of the LSS with a theoretical worldwide coverage, as well as with no need for onsite instrumentation or even direct access; however, a good density of measurement points in both ascending and descending geometry is necessary. The combined use of VIM and traditional investigations can provide a more accurate LSS model.

A Mw=7.0 earthquake occurred in Haiti on January 12, 2010 (IMMmax=X) claiming at least
230,000 lives, injuring 100,000 people and leaving 600,000 homeless. They add to another sizeable portion of
the population already affected by previous disasters and political upheaval. It is not the first time -nor will be
the last- that a major earthquake hits Haiti. Damage and economic losses amount to over US$ 8 billion (120%
of 2009 GNP). The earthquake also triggered liquefaction and more than 7,000 landslides, most of them rock
and soil slides on 25-65° slopes within heavily fractured limestone, weathered basalt and basaltic breccia. The
most common slope movements were slow/continuous deformation-translation (curved/planar) slides and rapid
movements (rock-falls, regolith slumps, torrential debris flows), sometimes reactivated by intense rainfall and
aftershocks. Considering the need for a rapid determination of slope instability hazards to assist the reconstruction
process, “screening” macro-zonation methodologies were applied. The precision of basic data conditioned
the quality, accuracy and reliability of the outputs. These preliminary views must be verified in situ and refined
as new site specific data becomes available. The results must be used with discernment, being clear that these
are not slope stability quantitative determinations but only a first attempt at identifying areas prone to slope instability.

Landslide susceptibility, hazards, and risks have been extensively explored and analyzed in the past decades. However, choosing relevant conditioning factors in such analyses remains a challenging task. Landslide susceptibility mapping employs topological, environmental, geological, and hydrological parameters. Some researchers assume that as the number of conditioning factors increases, the precision of the generated susceptibility map increases. By contrast, other case studies prove that a small number of conditioning factors are sufficient to produce landslide susceptibility maps with a reasonable quality. This study investigates the effects of conditioning factors on landslide susceptibility mapping. Bukit Antarabangsa, Ulu Klang, Malaysia was selected as the study area, because it is a catchment area with a high potential of landslide occurrence. A spatial database of 31 landslide locations was evaluated to map landslide-susceptible areas. Two datasets of conditioning factors were constructed in GIS environment. The first dataset was derived from high-resolution airborne laser scanning data (LiDAR), which contains eight landslide conditioning factors: altitude, slope, aspect, curvature, stream power index (SPI), topographic wetness index (TWI), topographic roughness index (TRI), and sediment transport index (STI). The second dataset was gathered by using the same conditioning factors of the first dataset, but with the addition of other conditioning factors: geological and environmental factors of soil, geology, land use/cover (LULC), distance from river, and distance from road. Two different datasets were constructed to compare the efficiency of one over the other in landslide susceptibility zonation. Three methods were implemented to recognize the importance of different conditioning factors in landslide susceptibility mapping. Three different types of models such as weights-of-evidence (WoE) (bivariate statistical analysis), logistic regression (LR) (multivariate statistical analysis), and data-driven support vector machine (SVM) were used to determine the optimal landslide conditioning factors. The area under curve (AUC) was used to assess the obtained results. The prediction rates of WoE, LR, and SVM obtained from only the LiDAR-derived conditioning factors were 59%, 86%, and 84%, respectively. The prediction rates of the WoE, LR, and SVM obtained from the second dataset were 65%, 66%, and 69%, respectively. The LiDAR-derived conditioning factors were more sufficient in generating an accurate landslide susceptibility map. Using additional factors, such as geology, LULC, and so on, does not significantly increase the accuracy of the map. The findings of this study can be used as reference for future analysis in selecting data for landslide conditioning factors.

Abstract The main objective of the study was to determine the landslide hazard in the
drainage basin of the Niraj River taking into account as far as possible the dichotomous
relationship between space and time. A broad inventory of the Romanian approaches
concerned with natural hazard prediction reveals a clear preference for spatial analysis,
while the temporal scale was almost totally neglected. To fill this gap, the proposed
methodological approach combines GIS techniques for quantitative analysis with statistical
analysis and detailed observation in the field, both directly and indirectly through remote
sensing. Niraj River drainage basin is extended over an area of 658 km2 in the east-central
part of Transylvania Depression, one of Romania’s major geographic units. Due to the
substrate composition, consisting essentially of marls, clays and sands, this area is often
affected by rotational and shallow landslides causing important material damages. The first
step of this approach was to develop a spatial model for determining the susceptibility to
landslide occurrence related to several causative factors: lithology, geomorphology,
structural, hydroclimatic, seismic, land use and anthropogenic factors. In order to develop the temporal side of the prediction, two subsidiary objectives were assumed. The first
one, to obtain a more detailed database which contains also the temporal moments of the
landslides activation or reactivation. Secondly, to realise a statistical analysis of cumulative
rainfall, over a period of 90 days prior to the time of each sliding event, which
allows us to identify the correlation between cumulative rainfall which had triggered
recorded landslides (beginning with the year 2005) and the landslide occurrence moments.
On this basis, the main results obtained were: (1) the identification the recurring
interval of each value of precipitation amount; (2) the assessment of the temporal
probability of landslide occurrence, accepting the assumption that the precipitation
amount which led to landslide activation in the past will have the same effects in the
future; and (3) the development of four scenarios of landslide occurrence, starting from
the events of May 2005, April 2006, July 2010 and February 2013, which were used to
determine the annual probability of landslide occurrence under similar precipitation
conditions. Finally, by identifying the correlation curves between the probability of
landslides and time, taking into account the susceptibility classes, additional scenarios
were determined for representative years in the future (2021, 2050, 2071 and 2100). The
probability values were used to create correlation curves for each susceptibility class, the
corresponding mathematical expressions facilitating the computation of annual probability.
The results from the study area highlight the fact that there is an acceleration
trend of landslide processes in the high susceptibility area.
Keywords Hazard assessment  Landslide  Predictive curve  Susceptibility  Prediction 
Statistical analysis  GIS modelling

An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16 pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making.

The present research focuses on evaluating the effectiveness of landslide susceptibility using fuzzy gamma operator (FGO) and analytical hierarchy process (AHP) model in part of Western Ghat, India. The landslide inventory data and various causative factors were used to generate the landslide susceptibility index (LSI) map using geospatial technology. The LSI map classified into five classes and finally validated by Area Under Curve (AUC) method for accuracy assessment. The AUC result of success and prediction rates depicts that FGO model has high accuracy value than AHP model. However, both models are highly correlated (r = 0.976). Among the all causative factors slope, lithology, land use land cover, geomorphology and lineament proximity are strongly correlated (r = > 0.9) with the landslide susceptibility between the two models. The research outcome will be helpful for future spatial planning in the study region.

Landslides are a common natural hazard worldwide with greater socioeconomic impacts in developing and tropical countries. In Central America and Costa Rica, this phenomenon is mainly triggered by seismicity and extraordinary rainfall. In order to portray the damaging landslides, that caused human and material losses in Costa Rica, DesInventar disaster database was used to analyze damaging landslides reports from 1970 to 2018. Moreover, different generalized linear models (GLM) were performed to analyze the landslide hazard, vulnerability and risk in all the municipalities of the country. An Akaike Information Criterion (AIC) backward selection was used to contrast and determine the best hazard and vulnerability models. From a total of ten variables, terrain ruggedness index, 5 year intensity-duration-frequency precipitation curves and earthquakes distribution determined the landslide hazard. Otherwise, population, municipality area and Social Development Index are the most suitable variables to explain the landslide vulnerability. Subsequently, the multiplication between alternative landslide hazard and vulnerability indexes produced the risk index. Consequently, the highest risk values were obtained for large and rural municipalities (Pérez Zeledón, San Carlos, and Turrialba) as well as for densely populated and urban units (Alajuela, Desamparados, and Cartago. Results are critical for disaster risk reduction public institutions and academic stakeholders. Therefore, this methodology could be an interesting opportunity for different tropical and developing countries to achieve national or regional analyses of the most important risk component in each municipality and implement risk reduction strategies adapted for each municipality characteristics.

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibilitymaps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using land- slide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR mod- els using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting land- slide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of riskmitigation is achieved in an efficient manner.

In landslide susceptibility assessment, an important issue is the correct identification of
significant causal factors, which leads to the improvement of predictions regarding this
type of geomorphological processes. In the scientific literature, different weightings are
assigned 5 to these factors, but with large variations. This study aims to identify the spatial
variability and range of variation of landslide causal factors in different geographical
conditions. Four square sectors of 15km×15km (225 km2) were selected for analysis
from representative regions in Romania in terms of spatial extent of landslides, situated
both in hilly areas (Transylvanian Plateau and Moldavian Plateau) and lower mountain
10 region (Subcarpathians). The following factors were taken into consideration: elevation,
slope angle, slope height, terrain curvature (mean, plan and profile), distance from
drainage network, slope aspect, surface lithology and land use. For each sector, landslide
inventory, digital elevation model and thematic layers of the mentioned predictors
were achieved and integrated in georeferenced environment. The logistic regression
15 was applied separately for the four study sectors, as statistical method for assessing
terrain landsliding susceptibility. Maps of landslide susceptibility were achieved, the values
of which were classified using the natural breaks method (Jenks). The accuracy
of logistic regression outcomes was evaluated using the ROC curve and AUC parameter,
which show values between 0.852 and 0.922. The values of factor weights are
20 generally placed within the limits specified by the scientific literature. For all study sectors,
the prevailing factors for landslide susceptibility are slope angle, land use and
slope height above channel network. The study points out that the weights assigned
to the causal factors through logistic regression are capable to reveal some important
regional characteristics in landslides manifestation.

Landslide susceptibility mapping is indispensable for disaster management and planning development operations in mountainous regions. The potential use of light detection and ranging (LiDAR) data was explored in this study for deriving landslide-conditioning factors for the spatial prediction of landslide-susceptible areas in a landslide-prone area in Ulu Klang, Malaysia. Nine landslide-conditioning factors, such as altitude, slope, aspect, curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), sediment transport index (STI), and slope length (SL), were directly derived from LiDAR for landslide-susceptibility mapping. The main objective of this research was to propose a novel ensemble landslide susceptibility mapping method to enhance the performance of individual methods of support vector machine (SVM) and evidential belief function (EBF). SVM is time-consuming when various data types, such as nominal, scale, and ordinal, are used. This characteristic of the individual SVM method is not optimal for hazard modeling. This drawback can be resolved by assessing the effects of the classes of each conditioning factor on landslide occurrence through a datadriven EBF method. Hence, EBF was applied in this study, and weights were acquired for the classes of each conditioning factor. The conditioning factors were reclassified based on the attained weights and entered into SVM as a scale to evaluate the correlation between landslide occurrence and each conditioning factor. Four SVM kernel types [radial basis function kernel (RBF), sigmoid kernel (SIG), linear kernel (LN), and polynomial kernel (PL)] were tested to explore the efficiency of each kernel in SVM modeling. The efficiencies of the ensemble EBF and SVM methods were examined through area under curve (AUC). The RBF kernel obtained better results than the other kernel types. The success and prediction rates obtained from the validation results of ensemble EBF and RBF-SVM method were 83.04% and 80.04%, respectively. The proposed novel ensemble method reasonably accelerated the processing and enhanced the results by combining the advantages of both methods.

Rock fall hazard assessment is a very demanding problem because of the complexity of the involved physics and its intrinsic stochasticity. New powerful tools, developed in the last few years, are characterized by different degrees of completeness, models, and algorithms. In the following, the modelling approach developed through the years within the code Hy_Stone is presented. Some issues related to the sensitivity to 3D topographic descriptions, the effects of stochasticity on model results and the calibration of 3D rockfall simulation are presented.

Due to the city’s geographical context and human intervention, landslides occur in Zalău Municipality on extended areas. With a medium reactivation potential, some of these processes repeatedly affect dwellings, elements of infrastructure or agricultural terrains. The main purpose of this paper is to identify and locate the landslide prone areas from Zalău based on the landslide susceptibility assessment performed with the help of the semi-quantitative method included in the Governmental Decision 447/2003 – Mapping methodology and content of landslide and flood risk maps. The estimation of the value and the geographical distribution for each susceptibility coefficient was performed separately for the lithologic, geomorphologic, structural, hydrologic and climatic, hydrogeologic, seismic, sylvic and anthropic factors. Using GIS techniques, the thematic maps representing the contribution of each factor to landslide occurrence and evolution were used to determine the map of average susceptibility coefficient. The validation was achieved by comparing the results with the location of active landslides identified in the field and through cartographic analysis of topographic maps and satellite images. Identifying landslide prone areas is a necessary stage in the process of landslide prevention and mitigation of negative effects.

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

Goncha Siso Eneses area of East Gojam Zone in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent landslide occurrences causing fatalities and damages in cultivated and non-cultivated lands, infrastructure and properties. Hence, preparing a landslide susceptibility map is very helpful in reducing the damages in infrastructure and properties and loss of animal and human lives. In this study, GIS-based information value and logistic regression models were applied. A reliable and detailed landslide inventory with 894 landslides was prepared through detailed fieldwork and Google Earth image interpretation. These landslides were randomly divided into training data set for model development and testing data set for model validation. Nine landslide causative factors like slope, curvature, aspect, lithology, distance to stream, distance to lineament, distance to spring, rainfall and land use/cover were integrated with training landslides to determine the weight(s) of each landslide factor and factor classes using Information Value and Logistic Regression models, respectively. The landslide susceptibility index map was then produced by summing the weights of all the landslide factors using raster calculator of the spatial analyst tool in GIS. To evaluate the performance of the information value and logistic regression models for landslide susceptibility modeling, the relative landslide density index and area under the curve (AUC) of the receiver operating characteristic curves were performed on both the training and testing landslide data sets. The model has an AUC accuracy of 88.9% success rate and 85.9% prediction rate for information value model whereas 81.8% success rate and 80.2% predictive rate for logistic regression model.

This deliverable provides a series of case studies on various aspects related to the use of Geographic Information Systems (GIS) for landslide inventory mapping, landslide susceptibility, hazard, vulnerability and risk assessment. This training package contains 12 case studies from 4 different countries and using different scales of analysis. Each of the case studies comes with a complete dataset that users can download from the SafeLand website, or from the ITC ftp site. The case studies are based on an Open Source GIS and Image Processing software (ILWIS) which allows users to practice with the methods without the restriction of buying proprietary software. The software can also be downloaded together with the data files. The exercise descriptions are fully worked out and described, and each exercise can be carried out individually, without the need to follow them in a certain order. The target group for this training package on GIS for landslide risk assessment conists of University students, PhD researchers and practioners on landslide hazard and risk assessment.
Although the methods have been often simplified, they do go a good idea of what the capabilities are of using GIS for collection landslides inventories , monitoring changes through satellite images and Digital Elevation Models, analyse the initiation susceptibility of
landslide using different methods (heuristic, statistical, physically based modelling), carry out run-out modelling, perform a rainfall threshold analysis, and perform both qualitative and quantitative risk assessments at different scales. Several of the case study areas that have been investigated in the SafeLand project (e.g. Barcelonnette in France, Nocera in Italy), and also in the two countries associated with the project (China and India). The training materials can be used by individuals as personal training, or can be used in training courses. In that case a reference to the project and to the partner that generated the training package is required.
See also: ftp://ftp.itc.nl/pub/westen/Safeland/

Safe operations of forest practices in mountainous regions require effective development planning to mitigate hazards posed by landslides. British Columbia, Canada, has for the past 2 decades implemented landslide risk management policies aimed at reducing the impacts of the forestry industry on landslides. Consequently, it is required that timber harvesting sites be evaluated for their potential or existing impacts on terrain stability. Statistical landslide susceptibility modelling can enhance this evaluation by geographically highlighting potential hazardous areas. In addition, these statistical models can also improve our understanding of regional landslide controlling factors. The purpose of this research was to explore the regional effects of forest harvesting activities, topography, precipitation and geology on landslides initiated during an extreme rainfall event in November 2006 on Vancouver Island, British Columbia. These effects were analyzed with a nonparametric statistical method, the generalized additive model (GAM). Although topography was the strongest predictor of landslide initiation, low density forest interpreted as regrowth areas and proximity to forest service roads were jointly associated with a 6- to 9-fold increase in the odds of landslide initiation, while accounting for other environmental confounders. This result highlights the importance of continuing proper landslide risk management to control the effects of forest practices on landslide initiation.

Landslide hazard zonation map of Kurram valley shows that Gharbina village at Bulbulaq, Parachinar lies within the high landslide prone region. Geological investigations of the landslide/rotational slump and debris-flow at study area indicate that the inhabited property at Gharbina village is vulnerable to a devastating landslide. The total area of the slump that had badly affected wheat crops, orchards, poplar and walnut trees was approximately 19.6 hectare. It had also disrupted 118 meters dirt road, which connecting Gharbina, Khachan, Dand and Rajju villages to Parachinar city. Walls of the houses at Gharbina village are cracked and leaned. The affected area and the regions shown on landslide hazard zonation map of Kurram valley might be declared an area prone to landslide/slumping. The Gharbina village, which is badly affected during slump, should be permanently evacuated because the northward drift of the main scarp of the slump and southward movement along transverse fractures in the crown region may cause fatalities and infrastructure damages. Knowing the nature of the problem and the vulnerability of the area for slope failure, it is recommended that evacuation of the high hazard zone should be carried out as top priority to ensure safety of the inhabitants.

Keywords: statistical modelling; slope movements; geophysical simulation; susceptibility; validation.

Is presented and discussed the susceptibility maps to the translational landslides in the Meia Légua
watershed located at Peso da Régua and Santa Marta de Penaguião based on the methods of
Information Value and Logistic Regression. It was made a simulation of slope instability before
the implementation of agricultural terraces using the physically based SHALSTAB model.
It is evaluated and compared the predictive capability of the models by validation through success
rate curve, ROC curve and contingency tables diagnosis rates.
The susceptibility modelling used two different contribution areas calculation method (m2)
Multiple Flow Direction (MD8) algorithm – Model A –and D-Infinity (D ∞) algorithm – Model
B.
The susceptibility simulation presented prospective models for one ground frame intervention
project: agricultural terraces from 1 and 2 vineyard bards (2,5 and 3,5m width, respectively). For
each type of agricultural terraces 3 models were produced (A, B and C) with internal friction
angle from 20, 18 and 15º.
These models were extracted for 8 areas reconverted in 2013 to land embankments terraces built
between ancient terraces post-phylloxera walls from study area.
According validation the statistical based model with higher predictive capability is Model A
from Information Value with com 73% on the RVP e 75% in the AAC according to the success
rate curve.
On the simulation scenarios the B and C models had higher prediction on the RVP and the model
B was better due to the lower RFP. However we conclude that all may be used in preventive
intervention, according the characteristics of the terrain and type of the programed intervention.
We concluded that 1 vineyard bard agricultural terraces are less susceptible to instability
processes. Also we locate the areas where can be installed terraces with 2 or only 1 vineyard bard.

This study proposes a method to determine
zones gentler or steeper than the equilibrium slope angle
based on rock mass strength (RMS) and to regionalize the
parameters. The method was developed using GIS and
applied to rock masses of the Alpujarride Complex (Granada,
Spain). 41% of the natural slopes studied have angles
lower than the equilibrium due to dissolution processes and
the presence of alluvial or colluvial deposits, while 28% of
those slopes have angles higher than the equilibrium due to
rapid erosion in tectonically active zones. Cross correlation
analysis between maps showing degrees of slope equilibrium
and landslide inventories suggest that long-term
geomorphic changes have induced slope angles higher than
the equilibrium which are associated with rock falls and
rock slides.

Landslides represent one of the most morphodynamic processes that affect the steep lands, and may destroy croplands as
well as urban and industrial development. Landslide risk analysis can help government agencies to select suitable locations for
development schemes and plan mitigation measures in unstable landslide-prone areas. This study describes an approach for
assessing the landslide risk potential, mainly for shallow landslides, with reference to Jabonosa river basin in the Venezuelan
Andes using artificial neural networks (ANNs), specifically a Multilayer Perceptron with backpropagation learning algorithm.
The approach developed uses a wide range of parameters of slope instability derived from digital elevation models (DEMs),
remote sensing imagery and documentary data. Around 90% overall accuracy produced by the ANN technique were found
promising for future studies in landslide susceptibility zonation.

Globally, natural hazards have become more destructive in recent times because of rapid urban development and exposure. Consequently, significant human life loss, the damage to property and infrastructure, and the collapse of the environment directed the attention of geoscientists to control the consequences and risk management in relation to geo-hazards. In this research, an effort was made to produce a compound map, geo-visualizing the susceptibility of multi-hazards, to select suitable sites for sustainable future development and other economic activities in the region. Muzaffarabad District was chosen as a case research area due to the high magnitude of hydro-meteorological and geological hazards. On the one hand, both selected geo-hazard inventories were developed using the field survey and remote sensing data. The subjective and objective weight of all the causative factors and their classes were calculated using the assembled geospatial techniques, such as the Analytical Hier...