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Landslides in mountainous areas render major damages to residential areas, roads, and farmlands. Hence, one of the basic measures to reduce the possible damage is by identifying landslide-prone areas through landslide mapping by different models and methods. The purpose of conducting this study is to evaluate the efficacy of a combination of two models of the analytical network process (ANP) and fuzzy logic in landslide risk mapping in the Azarshahr Chay basin in northwest Iran. After field investigations and a review of research literature, factors affecting the occurrence of landslides including slope, slope aspect, altitude, lithology, land use, vegetation density, rainfall, distance to fault, distance to roads, distance to rivers, along with a map of the distribution of occurred landslides were prepared in GIS environment. Then, fuzzy logic was used for weighting sub-criteria, and the ANP was applied to weight the criteria. Next, they were integrated based on GIS spatial analysis methods and the landslide risk map was produced. Evaluating the results of this study by using receiver operating characteristic curves shows that the hybrid model designed by areas under the curve 0.815 has good accuracy. Also, according to the prepared map, a total of 23.22% of the area, amounting to 105.38 km 2 , is in the high and very high-risk class. Results of this research are great of importance for regional planning tasks and the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area. [ABSTRACT FROM AUTHOR]

In this study, in order to map storm event-induced landslide susceptibility for central Taiwan, remote-sensing data were used to interpret landslides using a geographical information system (GIS). Discriminant analysis was used to establish rules that can successfully classify landslide and nonlandslide slopes. GIS-based slope units were used as the terrain subdivision to determine landslide susceptibility. A set of causative factors and a triggering factor were used to build a landslide prediction model. Data for shallow landslides triggered by three crucial rainfall events in July 2001, July 2004, and June 2006 were selected for developing the model. Among the factors used, the total accumulated rainfall is found to be a key factor in the interpretation of landslides. The landslide susceptibility maps were built for the Guoshin region and validated using predicting samples and data from a subsequent storm event, the 2008 Sinlaku typhoon. Results show that the developed model is capable of predicting the shallow landslide distribution induced by heavy rainfall in the study area. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology for validating landslide susceptibility results in the Pinggu district (Beijing, China). A landslide inventory including 169 landslides was prepared, and eight factors correlated to landslides (lithology, tectonic faults, topographic elevation, slope gradient, aspect, slope curvature, land use, and road network) were processed, integrating two techniques, namely the frequency ratio (FR) and the certainty factor (CF), in a geographic information system (GIS) environment. The area under the curve (success rate curve and prediction curve) analysis was used to evaluate model compatibility and predictability. Validation results indicated that the values of the area under the curve for the FR model and the CF model were 0.769 and 0.768, respectively. Considering spatial correlation, an alternative complementary method for validating landslide susceptibility maps was introduced. The spatially approximate maps could be discriminated from their matrices which carry structural information, and the structural similarity index (SSI) was then proposed to quantify the similarity. As a specific example, the SSI value of the FR (74.15%) scored higher than that of the CF model (69.36%), demonstrating its promise in validating different landslide susceptibility maps. These results show that the FR model outperforms the CF model in producing a landslide susceptibility map in the study area. [ABSTRACT FROM AUTHOR]

This paper outlines a framework for risk assessment of landslides triggered by earthquakes and rainfall in urban buildings in the city of Medellín - Colombia, applying a model that uses a geographic information system (GIS). We applied a computer model that includes topographic, geological, geotechnical and hydrological features of the study area to assess landslide hazards using the Newmark's pseudo-static method, together with a probabilistic approach based on the first order and second moment method (FOSM). The physical vulnerability assessment of buildings was conducted using structural fragility indexes, as well as the definition of damage level of buildings via decision trees and using Medellin's cadastral inventory data. The probability of occurrence of a landslide was calculated assuming that an earthquake produces horizontal ground acceleration ( A h ) and considering the uncertainty of the geotechnical parameters and the soil saturation conditions of the ground. The probability of occurrence was multiplied by the structural fragility index values and by the replacement value of structures. The model implemented aims to quantify the risk caused by this kind of disaster in an area of the city of Medellín based on different values of A h and an analysis of the damage costs of this disaster to buildings under different scenarios and structural conditions. Currently, 62% of “Valle de Aburra” where the study area is located is under very low condition of landslide hazard and 38% is under low condition. If all buildings in the study area fulfilled the requirements of the Colombian building code, the costs of a landslide would be reduced 63% compared with the current condition. An earthquake with a return period of 475 years was used in this analysis according to the seismic microzonation study in 2002. [ABSTRACT FROM AUTHOR]

The aims of this study were to apply and validate a logistic regression model for landslide hazard, considering rainfall probability and using a geographic information system. The study focused on the Deokeokri and Karisanri areas of Inje, South Korea. We chose logistic regression for its mathematical rigor and its use of implementation in GIS software. Rainfall probability is analyzed for a quantitative prediction of rainfall changes in the study area. The rainfall probability was calculated using the Gumbel distribution. Then, the probabilities of landslides in the study area in target years (1, 3, 10, 50, and 100 years in the future) were calculated assuming that landslides are triggered by daily rainfall of 202 mm or 3-day cumulative rainfall of 449 mm. Landslide hazard maps were developed for the two study areas, and the logistic regression coefficients for one area were applied to the other area to validate the method. In Karisanri, all recorded landslides were used for validation. Validation results for the 202-mm daily precipitation threshold in Karisanri showed an average accuracy of 79.14 %, whereas those for the 449-mm 3-day cumulative precipitation threshold showed an average accuracy of 81.31 %. A combination of rainfall probability and logistic regression with a GIS is an effective method for analyzing the possibility of future landslides. [ABSTRACT FROM AUTHOR]

Bedding planes are a known factor that controls the type, abundance and pattern of landslides. Where layered rocks crop out, the geometrical relationships between the attitude of the bedding and the geometry of the terrain is crucial to understand landslide phenomena. Obtaining information on bedding attitude for large areas through field surveys is time-consuming, and resource intensive, hampering the possibility of quantitative investigations on the control of bedding planes on landslides. We propose a GIS-based method to extract information on bedding planes from the analysis of information captured through the visual interpretation of stereoscopic aerial photographs and a digital representation of the terrain. We tested the method in the Collazone study area, Umbria, Central Italy, where we used spatially distributed information on beddings and terrain information obtained from a 10 × 10-m DEM to determine morpho-structural domains. We exploited the morpho-structural terrain zonation, in combination with landslide information for the same area, to investigate the role of beddings in controlling the distribution and abundance of landslides in the study area. We found that beddings condition the location and abundance of relict and deep-seated landslides, most abundant in cataclinal slopes, and do not condition significantly the shallow landslides. We expect the method to facilitate the production of maps of morpho-structural domains in layered geological environments. This will contribute to a better understanding of landslide phenomena and to foster the preparation of advanced landslide susceptibility and hazard models. [ABSTRACT FROM AUTHOR]

A decision tree approach was applied and validated for analysis of landslide susceptibility using a geographic information system (GIS). The study area was the Pyeongchang area in Gangwon Province, Korea, where many landslides occurred in 2006 and where the 2018 Winter Olympics are to be held. Spatial data, such as landslides, topography, and geology, were detected, collected, and compiled in a database using remote sensing and GIS. The 3994 recorded landslide locations were randomly split 50/50 for training and validation of the models. A decision tree model, which is a type of data-mining classification model, was applied and decision trees were constructed using the chi-squared (χ2) automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. Also, as a reference, a frequency-ratio model was applied using the same database. The relationships between the detected landslide locations and their factors were identified and quantified by frequency-ratio and decision tree models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indices and maps. Then, the resulting landslide-susceptibility maps were validated using area-under-the-curve (AUC) analysis with the landslide area data that had not been used for training the model. The decision tree models using the CHAID and QUEST algorithms had accuracies of 81.56% and 80.91%, respectively, which were somewhat better than the results for the frequency-ratio model (80.15%). These results indicate that decision tree models using the CHAID and QUEST algorithms can be useful for landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

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. [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]

This work evaluates the capability of Web Processing Service (WPS) to foster the shallow landslides hazard awareness. In particular this study demonstrates how the WPS can provide an ideal environment for the development of real-time applications by creating a process chain able to couple two widely used models, TRIGRS and DFWALK, respectively applied for (i) the localization and characterization of areas susceptible to shallow landslides and (ii) the assessment of these landslides run-out in ease of collapse. The service has been built with the PyWPS software by integrating the models through the GIS GRASS. The application, tested for the case study of Ha Giang (Vietnam), can be generally deployed and used both as service for shallow landslide hazard forecast and as a tool for the identification of vulnerable areas for emergency planning design. [ABSTRACT FROM AUTHOR]