Search

Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems. [ABSTRACT FROM AUTHOR]

Jember is surrounded and limited by highlands such as Mount Argopura, Mount Ijen, Mount Argopura, and the southern karst mountains. In 2015-2022, the Jember area flooded during the rainy season and dried during the dry season. Changes in land cover that do not follow the science of soil preservation will cause disasters, including landslides and erosion. The purpose of this study is to assess the risk of landslides on the slopes of Mount Argopura through the Geographic Information System. The study used a field survey method that was divided into several stages, including making a working map, conducting a field survey, and analyzing the data in the laboratory. The sensitivity analysis of the landslide level used as the basis for the assessment used the relationship between the parameters of soil erodibility, soil erosion, slope and soil conservation, and slope length. The sensitivity of the level of landslide risk on Mount Argopura is divided into five classes, from very light to very heavy. The very light category covers 4.92% of the total area with erosion of 0.47 t/ha/year. The very heavy class covers 39.70% of the total area, with 1,360.79 t/ha/year erosion. [ABSTRACT FROM AUTHOR]

Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms. [ABSTRACT FROM AUTHOR]

The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and −2 Log likelihood, Cox & SnellR-Square and NagelkerkeR-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making. [ABSTRACT FROM PUBLISHER]

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]

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas. [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]

Regional landslide assessments and mapping have been effectively pursued by research institutions, national and local governments, non-governmental organizations (NGOs), and different stakeholders for some time, and a wide range of methodologies and technologies have consequently been proposed. Land-use mapping and hazard event inventories are mostly created by remote-sensing data, subject to difficulties, such as accessibility and terrain, which need to be overcome. Likewise, landslide data acquisition for the field navigation can magnify the accuracy of databases and analysis. Open-source Web and mobile GIS tools can be used for improved ground-truthing of critical areas to improve the analysis of hazard patterns and triggering factors. This paper reviews the implementation and selected results of a secure mobile-map application called ROOMA (Rapid Offline--Online Mapping Application) for the rapid data collection of landslide hazard and risk. This prototype assists the quick creation of landslide inventory maps (LIMs) by collecting information on the type, feature, volume, date, and patterns of landslides using open-source Web-GIS technologies such as Leaflet maps, Cordova, GeoServer, PostgreSQL as the real DBMS (database management system), and PostGIS as its plug-in for spatial database management. This application comprises Leaflet maps coupled with satellite images as a base layer, drawing tools, geolocation (using GPS and the Internet), photo mapping, and event clustering. All the features and information are recorded into a GeoJSON text file in an offline version (Android) and subsequently uploaded to the online mode (using all browsers) with the availability of Internet. Finally, the events can be accessed and edited after approval by an administrator and then be visualized by the general public. [ABSTRACT FROM AUTHOR]

Through investigation and analysis of geological conditions and mechanical parameters of the Taziping landslide, the finite volume method was adopted, and, the rheological model was adopted to simulate the landslide and avalanche entire mass movement process. The present paper adopted the GIS platform to simulate the mass movement process before and after treatment. This paper also provided the conditions and characteristic parameters of soil deposits (thickness, speed, and stresses) during the landslide mass movement process and mapped the 3D division of hazard zones before and after landslide treatment. Results indicated that the scope of hazard zones contracted after engineering treatment of the landslide. The extent of high-hazard zones was reduced by about 2/3 of the area before treatment, and characteristic parameters of the mass movement process after treatment decreased to 1/3 of those before treatment. Despite engineering treatment, the Taziping landslide still poses significant hazard to nearby settlements. Therefore, we propose that houses located in high-hazard zones be relocated or reinforced for protection. [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]

This study focuses on revealing the landslide susceptibility of mainly the Kelemen Valley and adjacent valleys in the Western Black Sea Region of Turkey by investigating the parameters causing landslide occurrences in the region in a GIS environment and applying the frequency rate method on landslide-prone areas. Many buildings and houses in the study area, 382 houses to be exact, had to be evacuated by the General Directorate of Disaster Affairs of Turkey based on the findings from landslide studies conducted in the region since 1950 and were declared 'unsuitable for construction and residence due to disaster.' Therefore, it is of a vital significance to disclose the factors of landslide occurrences in the Kelemen Valley and consequently to prepare landslide susceptibility maps. For this purpose, 82 landslide exposed areas are determined by field surveys and checked against satellite imagery. All 82 landslides are found to have occurred in the Cretaceous Ulus Formation with the same climate and same vegetation, which are not considered distinctive parameters for the region. These findings have given rise to exclude these parameters from analyses on the basis that they are proclaimed as common parameters; instead, the parameters such as slope, aspect and distance to river are taken into consideration and assessed in ArcGIS™ software. Finally, the landslide susceptibility of the region has been obtained. Furthermore, a comparison is conducted between the existing landslides and the outcome map, and it is deduced that the map produced has a correlation ratio of 86 % with the existing occurrences for high-risk areas. This finding clearly dictates that landslide-prone areas need to be accounted for when planning for new settlement areas. [ABSTRACT FROM AUTHOR]

The main goal of this study is to produce landslide susceptibility maps by frequency ratio (FR) and index of entropy (IoE) models based on geographic information system (GIS) for the Daguan county of Yunnan province, China. At first, landslide locations were identified by earlier reports and aerial photographs as well as by carrying out field surveys, and a total of 194 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (136 landslide locations) for training the FR and IoE models and the remaining 30 % (58 landslides locations) were used for validation purposes. Fifteen landslide conditioning factors were constructed in GIS. The susceptibility maps produced using FR and IoE models were divided into five different susceptibility classes such as very low, low, moderate, high, and very high. Finally, the validation of landslide susceptibility maps was carried out based on training dataset and the validation data using the area under the curve (AUC) method. The success rate curve showed that the area under the curve for FR and IoE models were 0.8191 and 0.8109 accuracy, respectively. Similarly, the validation result showed that the prediction accuracy of the two models was 81.75 % for FR model and 81.44 % for IoE model, respectively. The maps produced by both models exhibits better satisfactory properties. The landslide susceptibility maps may be useful for planning purposes for environmental protection and natural hazard mitigation studies in the near future. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to produce reliable susceptibility maps using GIS-based support vector machine (SVM) models and compare their performances for the Qianyang County of Baoji City, Shaanxi Province, China. In this paper, with kernel classifiers of linear, polynomial, radial basis function and sigmoid, the four various types were applied in landslide susceptibility mapping. The important input parameters for the landslide susceptibility assessment were acquired from different sources. Firstly, 81 landslide sites were obtained by aerial photographs, earlier reports and field surveys. Then, the landslide inventory was randomly classified into two datasets: 70 % (56 landslides) for training the models and 30 % (25 landslides) for validation purpose. Secondly, 15 landslide conditioning factors were selected (i.e., slope angle, slope aspect, altitude, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, NDVI, STI, SPI, TWI, geomorphology, rainfall, and lithology). Subsequently, with four types of kernel function classifiers based on landslide conditioning factors, landslide susceptibility parameters were obtained using SVM models. Finally, the rationality of landslide susceptibility maps was verified using the receiver operating characteristics with both success rate curve and prediction rate curve. The validation results showed that success rates for the four SVM models were 83.15 % (RBF-SVM), 82.72 % (PL-SVM), 81.77 % (LN-SVM), and 79.99 % (SIG-SVM). The prediction rates for the four SVM models were 77.98 % (RBF-SVM), 77.50 % (PL-SVM), 77.07 % (LN-SVM), and 76.08 % (SIG-SVM), respectively. The results showed that the RBF-SVM model had the highest overall performance. [ABSTRACT FROM AUTHOR]

Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GIS-based frequency ratio (FR) and logistic regression (LR) models have been applied using landslide inventory and the nine landslide factors (i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is an important tool for disaster management and development activities such as planning of transportation infrastructure, settlement and agriculture. Selelkula area of the Jema River Gorge in Central Ethiopian Highland was chosen as a study area. Fuzzy logic (FL) and rock engineering system (RES) in a GIS environment were employed to prepare landslide susceptibility maps for this area. Data sets including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river and distance from lineament were generated using the field data, remote sensing and GIS. In FL, the fuzzy membership values were calculated by normalizing the frequency ratio values of each factor's class in a range between 0 and 1. Then, the fuzzy membership values were combined by fuzzy AND, fuzzy OR, fuzzy gamma, fuzzy sum and fuzzy product operators. A total of twelve landslide susceptibility maps were produced using FL and the best map was chosen based on area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the highest difference between minimum and maximum susceptibility index values. Based on these criteria, the landslide susceptibility map produced using fuzzy gamma ( γ = 0.8) operator was selected. RES is a semi-quantitative method which assigns each factor's classes a value between 0 and 4 based on the principles of Hudson (Rock engineering systems, theory and practice. Ellis Horwood, Chichester, ) and uses an interaction matrix through a coding system of five values in previous researches but a coding system of nine values has been used in this study to provide a wider range of values for interactions among each pair of landslide factors and each landslide factor with a landslide. Unlike other methods, RES has a freedom to evaluate and assign each interaction based on expert's knowledge and experience. But it also bears the problem of subjectivity in assigning the interaction values. On the other hand, FL is completely data driven and does not show any subjectivity in assigning values for each factor class. The prediction accuracies of FL and RES, which can be determined from ROC curves, were found to be 87.2 and 88.6 %, respectively. For validation purpose, the existing landslides were overlaid over the two landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes are slightly higher in RES than FL method and hence the former is more accurate in predicting the future landslide occurrence than the later. [ABSTRACT FROM AUTHOR]

A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system (GIS). Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region, which lies in the transitional area among Qinghai-Tibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides. Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases. Logistic regression (LR) was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use. The quality of the landslide susceptibility map produced in this paper was validated and the result can be used for designing protective and mitigation measures against landslide hazards. The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment. [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 main purpose of this research is to evaluate the modelling capability and predictive power of a bivariate statistical method for earthquake-triggered landslide susceptibility mapping. A weight index (Wi) model was developed for the 2008 Wenchuan earthquake region in Sichuan Province, China, using a wide range of optical remote sensing data, and carried out on the basis of a geographic information system (GIS) platform. The 2008 Wenchuan earthquake triggered 196007 landslides, with a total area of 1150.43 km2, in an approximately oblong area around the Yingxiu–Beichuan coseismic surface fault-rupture (the Yingxiu–Beichuan fault). The landslides of the study area were mapped using visual interpretation of high-resolution satellite images and aerial photographs, both pre- and post-earthquake, and checked in the field at various locations. As a consequence, a nearly complete inventory of landslides triggered by the Wenchuan earthquake was constructed. Topographic and geological data and earthquake-related information were collected, processed and constructed into a spatial database using GIS and image processing technologies. A total of 10 controlling parameters associated with the earthquake-triggered landslides were selected, including elevation, slope angle, slope aspect, slope curvature, slope position, lithology, seismic intensity, peak ground acceleration (PGA), distance from the Yingxiu–Beichuan fault, and distance along this fault. To assist with the development of the model, the complete dataset of 196007 landslides was randomly partitioned into two subsets; a training dataset, which contains 70% of the data (137204 landslides, with a total area of 809.96 km2), and a testing dataset accounting for 30% of the data (58803 landslides, with a total area of 340.47 km2). A landslide susceptibility index map was generated using the training dataset, the 10 impact factors, and the Wimodel. In addition, for a conditionally dependent factor analysis, seven other factor-combination cases were also used to construct landslide susceptibility index maps. Finally, these eight landslide susceptibility maps were compared with the training data and testing data to obtain model capability (success rate) and predictive power (predictive rate) information. The validation results show that the success and predictive rates of the Wimodelling exceeded 90% for the approaches that include the use of seismic factors. The final landslide susceptibility map can be used to identify and delineate unstable susceptibility-prone areas, and help planners to choose favourable locations for development schemes, such as infrastructure, construction and environmental protection schemes. The generic component of this research would allow application in other regions affected by high-intensity earthquakes and unstable terrain covering very large areas. [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]

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities. [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]

Road instability along the Jerash-Amman highway was assessed using the weighted overlay method in Geographic Information System environment. The landslide susceptibility map was developed from nine contributing parameters. The map of landslide susceptibility was classified into five zones: very low (very stable), low (stable), moderate (moderately stable), high (unstable), and very high (highly unstable). The very high susceptibility and high susceptibility zones covered 15.14% and 31.81% of the study area, respectively. The main factors that made most parts of study area prone to landslides include excessive drainage channels, road cuts, and unfavorable rock strata such as marl and friable sandstone intercalated with clay and highly fractured limestone. Fracture zones are a major player in land instability. The moderate and high susceptibility zones are the most common in urban (e.g., Salhoub and Gaza camp) and agricultural areas. About 34% of the urban areas and 28.82% of the agricultural areas are characterized by the high susceptibility zone. Twenty percent of the Jerash-Amman highway length and 58% of the overall highway length are located in the very high susceptibility zone. The landslide susceptibility map was validated by the recorded landslides. More than 80 of the inventoried landslides are in unstable zones, which indicate that the selected causative factors are relevant and the model performs properly. [ABSTRACT FROM AUTHOR]

As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides. [ABSTRACT FROM AUTHOR]