Prepare Landslide Susceptibility Map Research Articles

Landslide Susceptibility Analysis of Jugal Rural Municipality, Sindhupalchok

Hilly and mountainous areas of Nepal, with challenging terrain, young geology, and heavy monsoon rainfall, are susceptible to landslides and slope instability. To analyze and prepare landslide susceptibility maps, this study selects a typical hilly area, the Jugal Rural Municipality in Sindhupalchok district. Twelve factors contributing to landslides were considered, including slope, aspect, elevation, geology, land use, proximity to roads and drainage, plan curvature, profile curvature, NDVI (Normalized Difference Vegetation Index), soil type and rainfall. Moreover, 286 landslides were identified using high-resolution satellite imagery and field verification as the landslide inventory. These landslides were then randomly divided into two sets: 70% for training and 30\% for validation. Bivariate statistical analysis was performed using factor maps and the landslide inventory map. Notably, the analysis revealed a Prediction Rate (PR) of 9.35 for 'Land use', the highest among all factors considered. Since land use is a dynamic factor, we recommend conducting an analysis of land use changes and their impact on landslide susceptibility. Such an assessment would be invaluable during the planning and execution phases of development projects in Nepal's disaster-prone regions.

Comparative Study of Deep Neural Networks for Landslide Susceptibility Assessment: A Case Study of Pyeongchang-gun, South Korea

With an increase in local precipitation caused by extreme climatic phenomena, the frequency of landslides and associated damage has also increased. Therefore, compiling fine-scale landslide susceptibility assessment maps based on data from landslide-affected areas is essential. Deep neural network (DNN) and kernel-based DNN(DNNK) models were used to prepare landslide susceptibility maps of the mountainous Pyeongchang-gun region (South Korea) within a geographic information system framework. To map landslide susceptibility, datasets of landslide occurrence areas, topography, land use, forest, and soil were collected and entered into spatial databases, and 18 factors were then selected from the databases and used as model inputs. The training and test datasets consisted of 1600 and 400 landslide locations, respectively. The test accuracies of the DNN and DNNK models were 98.19% and 97.53% and 94.11% and 92.22% for the area under the receiver operating characteristic curve and the average precision value of the precision-recall curve, respectively. The location of future landslides can now be quickly and efficiently predicted using remote sensing data at a lower cost and with less labor. The landslide susceptibility maps produced in this study can play a role in sustainability and serve as references for establishing policies for landslide prevention and mitigation.

SUSCEPTIBILITY MAPPING OF RAINFALL INDUCED LANDSLIDES USING WEIGHT OF EVIDENCE METHOD IN JUMRE KHOLA WATERSHED OF PYUTHAN DISTRICT, NEPAL

The world’s development infrastructure is at risk from landslides and their consequences in many areas across the globe. There is evidence that landslide disaster is increasing in developing countries. Lesser Himalaya of Nepal is highly susceptible for landslide and various people lose their life and property each year due to landslide. The main aim of this study was to prepare 1) to identify the specific characteristics of landslides in Jumre Khola watershed. 2) To relate the rainfall intensity with landslides distribution in Jumre Khola watershed. & 3) To prepare landslide susceptibility map of Jumre Khola watershed. For this, a detailed landslide inventory was prepared by field survey and Google Earth imagery interpretation. This inventory map was separated into two groups for training the model and validating the result. A landslide susceptibility maps was developed by WOE model. Finally, validation of the model was done by using the success rate curve analysis. The verification result showed that the model performed very well with the success rate of 86.79%. The generalized linear model was performed between landslide and rainfall to know if there is significant relationship between landslide and rainfall. The Jumre Khola watershed is selected for the present study due to the presence of a large number of landslides in the region as well as due to the presence of huge amount of settlement in the mountain slopes and the river valley that are in high risk of landslides. The produced susceptibility map can be useful for general land-use planning, as well as it can provide information to the planners for the adaptation of appropriate mitigating measures for landside in the Jumre Khola watershed.

A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Türkiye)

Landslide susceptibility maps (LSMs) have been used frequently by researchers for many years in prediction of the occurrence of landslides. Since many landslides have occurred there in the past, Izmir, which is the third largest city of Türkiye, was selected for landslide susceptibility assessment using geographical information systems (GIS) and remote sensing (RS) techniques. The aim of this study is to create a better landslide susceptibility map (LSM) for the Izmir metropolitan area and its surroundings by minimizing the shortcomings of some of the commonly used methods. For this purpose, four different LSMs were prepared using the logistic regression (LR), analytical hierarchy process (AHP), frequency ratio (FR) and index of entropy (IOE) methods with susceptibility classes ranging from extremely low to extremely high. These four maps were then overlaid. The highest susceptibility class was chosen for each pixel to form a combined landslide susceptibility map (CLSM). The final CLSM is a thematic map presenting landslide susceptibility using five different classes. The geo-environmental factors selected for use in this analysis were slope angle, slope aspect, lithology, slope curvature, elevation, density of discontinuity, stream power index (SPI), land use and distance from stream. Finally, the areas under receiver-operating characteristic (ROC) curves were employed to compare the predictive capability of the five models used. Overall, the Combined Method (CM) (AUC = 0.887) performed very well for landslide susceptibility assessment. Out of all the models, the IOE model (AUC = 0.841) had a slightly lower predictive capability than the CM model, and AHP (AUC = 0.816) was better than FR (AUC = 0.738) and LR (AUC = 0.727). It was observed that, compared to rural areas, residential areas of Izmir city are particularly susceptible to landslides.

Expert based landslide susceptibility mapping for energy infrastructure planning

Landslides are a significant problem in the hilly region. Objective of this study is to prepare landslide susceptibility map of Almora district, India using expert weightage technique. The topography characteristics, geological characteristics and drainage characteristics of the region are considered as causative factors of landslides. The weightage to factors and sub-factors is assigned by expert opinion. Geographic information system is used to convert the causative factor maps in layers. The expert weightage are based on the judgment of expert and the historical landslide data. The weighted layers are overlaid in GIS environment and the final output map shows the landslide susceptibility in increasing order. The landslide susceptibility index varies from 7 to 45 in the region. Lower landslide susceptibility index (LSI) shows lower probability of occurrence of landslides and hence higher suitability for development of new infrastructure projects. The landslide susceptibility map can be used by planners and engineers.

A Comparative Study of Frequency Ratio, Shannon’s Entropy and Analytic Hierarchy Process (AHP) Models for Landslide Susceptibility Assessment

Landslide susceptibility maps are very important tools in the planning and management of landslide prone areas. Qualitative and quantitative methods each have their own advantages and dis-advantages in landslide susceptibility mapping. The aim of this study is to compare three models, i.e., frequency ratio (FR), Shannon’s entropy and analytic hierarchy process (AHP) by implementing them for the preparation of landslide susceptibility maps. Shimla, a district in Himachal Pradesh (H.P.), India was chosen for the study. A landslide inventory containing more than 1500 landslide events was prepared using previous literature, available historical data and a field survey. Out of the total number of landslide events, 30% data was used for training and 70% data was used for testing purpose. The frequency ratio, Shannon’s entropy and AHP models were implemented and three landslide susceptibility maps were prepared for the study area. The final landslide susceptibility maps were validated using a receiver operating characteristic (ROC) curve. The frequency ratio (FR) model yielded the highest accuracy, with 0.925 fitted ROC area, while the accuracy achieved by Shannon’s entropy model was 0.883. Analytic hierarchy process (AHP) yielded the lowest accuracy, with 0.732 fitted ROC area. The results of this study can be used by engineers and planners for better management and mitigation of landslides in the study area.

Landslide susceptibility zonation mapping using statistical index and landslide susceptibility analysis methods: A case study from Gindeberet district, Oromia Regional State, Central Ethiopia

The present study was carried out in Gindeberet district, central Ethiopia and covers a total of 128 km2. The geology of the area is comprised of sandstone, mudstone, limestone and basalt. The general objective was to evaluate the factors influencing the landslide initiation and to prepare a landslide susceptibility zonation map. One hundred thirty landslides were identified through field investigation and Google Earth image interpretation. Statistical Index Model (SIM) and Landslide Susceptibility Analysis (LSA) were used for the preparation of landslide susceptibility map. Lithology, slope steepness, aspect, land−use/land cover, and distance from drainage are the causative factors selected, whereas rainfall and human activities are considered as the triggering factors. The relations of landslides and causative factors were determined in terms of weighted value (Wij). From the lithology limestone, mudstone and weathered basalt have strong relationship with the landslides. Moreover, the result shows that weighted values (Wij) are high for N and W orientation class of slope aspect; greater than 45° slope steepness; agricultural land and moderately vegetated land−use/land cover class and 0−100 m and 100−200 m distance from drainage class. Landslide susceptibility zonation (LSZ) map was prepared and classified into very low, low, high, and very high susceptible zones. For LSZ map prepared by SIM model out of 130 landslides inventory data, 2 (1.54%) of the landslides fall in very low susceptible zone, 11 (8.46%) in low susceptible zone, 12 (9.23%) in moderate susceptible zone, 16 (12.31%) falls in high susceptible zone and 89 (68.46%) in very high susceptible zone, whereas LSZ map prepared by LSA model shows that out of 130 landslides inventory data, 11 (8.5%) of the landslides fall in very low susceptible zone, 12 (9.2%) in low susceptible zone, 15 (11.5%) in moderate susceptible zone, 25 (19.3%) in high susceptible zone and 67 (51.5%) in very high susceptible zone. The percent of existing landslide determined from LSZ maps prepared by SIM and LSA falls in high and very high susceptible zones were 81% and 71% respectively. The result of verification shows that areal distributions and occurrences of landslides were relatively comparable in both methods.

Sağlık Mahallesi Heyelanı (Ordu, TÜRKİYE)

Landslides cause loss of life and property from time to time. In Turkey, the Black Sea Region is the region where landslides are seen most frequently. In this study, Sağlık Neighborhood Landslide that occurred on May 15th, 2019 in the Central Black Sea Region was examined. This study aims to investigate the formation of the landslide and to discuss measures that can be taken in order to minimize the damage. For this purpose, area was visited and measurements were performed, soil samples and photos were taken. The region which has a rough terrain, receives precipitation throughout the year. The site has volcanic bedrock and thick debris and ground cover on it. The liquefaction limit of the soil is between 45.7 and 47.8 and the plasticity limit is between 20.51 and 22. Also, turning forests into agricultural areas, opening roads crossing the slopes, additional weights created by houses, water leaking from the sewer network to the ground and vibrations created by passing vehicles are among main causes of landslides. Stress cracks that started to occur in the crown part of the landslide three years ago started to expand on the day of the incident. The authorities who realized this evacuated the houses being under threat. Despite this, 22 buildings were destroyed and 8 hectares of agricultural land were damaged. To prevent damages, it has been proposed to prepare landslide susceptibility maps, to build new settlements away from potential landslide areas and to move residences in high-risk areas to safe places.

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps (LSMs) using computer-based advanced machine learning techniques and compare the performance of the models. To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model (CNN) and three popular machine learning techniques, i.e., random forest model (RF), artificial neural network model (ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and non-landslide points. A ratio of 70:30 was considered for the selection of both training and validation points. Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve and statistical methods like root mean square error (RMSE) and mean absolute error (MAE). In both datasets (training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.

An Assessment of the Use of an Advanced Neural Network Model with Five Different Training Strategies for the Preparation of Landslide Susceptibility Maps

Data collection for landslide susceptibility modelling is often an almost inhibitive activity. This has been the reason for quite sometimes land slide was described and modelled on the basis of spatially distributed values of landslide related attributes. This paper presents landslide susceptibility analysis at Selangor area, Malaysia, using artificial neural network model with the aid of remote sensing data and geographic information system (GIS) tools. To meet the objectives, landslide locations were identified in the study area from interpretation of aerial photographs and supported with extensive field surveys. Then, the landslide inventory was grouped into two categories: (1) training data (2) testing data. Further, topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS tools and image processing techniques. Nine landslide occurrence attributes were selected and analyzed using an artificial neural network model to generate the landslide susceptibility maps. Landslide loca tion data (training data) were used for training the neural network and five training sites were selected randomly in this case. The use of five training sites ensemble to investigate the model reliability, including the role of the thematic variables used to construct the model, and the model sensitivity to changes in the selection of the training sites. By studying the variation of the neural network’s susceptibility estimate, the error associated with the model is determined. The results of the neural network analysis are shown on five sets of landslide susceptibility maps. Then the susceptibility maps were validated using ”receiver operating characteristics (ROC)” method as a measure for the model verification. Landslide training data which were not used during the training of the neural network was used for the verification of the maps. The results of the analysis were verified using the landslide location data and compared between five different cases. Qualitatively, the model seems to give reasonable results with accuracy observed was 87%, 83%, 85%, 86% and 82% for five different training sites respectively.

Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility

Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of Alborz province, Iran, using hybrid Machine Learning (ML) algorithms, i.e., k-fold cross validation and ML techniques of credal decision tree (CDT), Alternative Decision Tree (ADTree), and their ensemble method (CDT-ADTree), which have been state-of-the-art soft computing techniques rarely used in the case of landslide susceptibility assessments. In this study, 22 key landslide causative factors (LCFs) were considered to explore their spatial relationship to landslides, based on local geomorphological and geo-environmental influences. The Random Forest (RF) algorithm was used for the identification of variables importance of different LCFs that are more prone to landslide susceptibility. A receiver operation characteristics (ROC) curve with area under the curve (AUC), accuracy, precision, and robustness index was used to evaluate and compare landslide susceptibility models. The output of the model performance shows that the CDT-ADTree model is the more robust model for the landslide susceptibility where the AUC, accuracy, and precision are 0.981, 0.837, and 0.867, respectively, than the standalone model of CDT and ADTree model. Therefore, it is concluded that the CDT-ADTree ensemble model can be applied as a new promising technique for spatial prediction of the landslide in further studies.

LANDSLIDE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST MODEL FOR ORDU, TURKEY

Abstract. Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.

Geomorphic diversity and landslide susceptibility in the Balason River Basin, Darjeeling Himalaya

This study attempts to assess the role of basin morphometric parameters in slope instability using a morphometric diversity (MD) model, as well as the role of drainage parameters and relief parameters in slope failure using drainage diversity (DD) and relief diversity (RD) models, respectively. For this, a total of 14 morphometric data layers were considered. The relationship of each data layer to landslide susceptibility was judged using a frequency ratio (FR) approach. Parameters like drainage density (Dd), drainage frequency (Df), relative relief (Rr), drainage texture (Dt), junction frequency (Jf), infiltration number (In), ruggedness index (Ri), dissection index (Di), elevation (E), slope (S), relief ratio (Rra) and hypsometric integral (Hi) were positively related with landslide potentiality while bifurcation ratio (Rb) and drainage intensity (Din) negatively correlated with S failure. The principal component analysis (PCA)-based weight assigned to each data layer in each model was multiplied with unidirectional reclassified data layers for each model using a weighted linear combination (WLC) approach to prepare landslide susceptibility maps. The receiver operating characteristics curve showed that the landslide prediction accuracy of the DD, RD and MD models were 71.4%, 73.9% and 76.3%, respectively. The FR plots of the aforesaid three models suggested that the chance of landslide increases from very low to very high in susceptible zones.

Application of analytic hierarchy process in landslide susceptibility mapping at regional scale in GIS environment

Landslides are very unpredictable and hazardous phenomenon in the hilly regions. The failure of slopes in hilly regions causes economic losses and fatalities. Aim of this study is to prepare a landslide susceptibility map of Kullu district, Himachal Pradesh (H.P.), India. The factors like slope, aspect, drainage characteristics, geological and lithological are considered for preparation of landslide susceptibility map. The weightage of causative factors are calculated using analytic hierarchy process (AHP). The weighted linear combination technique is used in GIS environment to calculate the landslide susceptibility index. The landslide susceptibility map is divided into four categories according to their susceptibility towards landslides. It is found that a significant area lies under high and very high susceptibility towards landslides. The results of the study can be used by the construction engineers, urban planners and environmental engineers.

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km2 (WofE& RBF-SVM), 474 km2 (WofE& Linear-SVM), 501km2 (WofE& Polynomial-SVM), and 498 km2 (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km2. The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts.

Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India

Landslide is an important geological hazard in the large extent of geo-environment, damaging the human lives and properties. The present work, intends to identify the landslide susceptibility zones for Darjeeling, India, using the ensembles of important knowledge driven statistical technique i.e. fuzzy logic with Landslide Numerical Risk Factor (LNRF) and Analytical Hierarchical Process (AHP). In the study area, 326 landslides have been identified and a landslide inventory map has been prepared based on these landslides. The landslide inventory map has considered as the dependent factor and the geo-environmental factors like rainfall, slope, aspect, altitude, geology, soil texture, distance from river, lineament and road, land use/ land cover, NDVI and TWI have been considered as independent factors. Landslide susceptibility maps were prepared based on the Fuzzy- Landslide Numerical Risk Factor (LNRF) and Fuzzy- analytic hierarchy process (AHP) methods in a GIS environment. According to the results of LNRF and AHP based fuzzy logic 34 and 22% areas are highly susceptible to landslide in this district. The landslide maps of both models have been validated through ROC curve and RMSE. The areas under curves are 91% (for Fuzzy-LNRF) and 90% (for Fuzzy-AHP) and RMSE values of these models are 0.18 and 0.14 which are indicating the good accuracy of both models in the identification of landslide susceptibility zones. Moreover, the Fuzzy-LNRF model is promising and sufficient to be advised as a method to prepare landslide susceptibility map at regional scale.

Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model

The assessment of landslide susceptibility is closely associated with the spatial distribution of previous landslides and landslide causative factors. In the present study, index of entropy (IOE) model was applied to map landslide susceptibility zones of Darjeeling Himalaya. This model is dealt with the relationship between landslide phenomena and landslide causative factors. To perform the model, 15 landslide conditioning data layers, i.e. elevation, aspect, slope, curvature, geology, soil, lineament density, distance to lineament, drainage density (DD), distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalised differential vegetation index (NDVI), and land use and land cover (LULC) were considered. Each and every class of landslide causative factor contributes a relative importance in landslide occurrences. To obtain the relative significance of each class of landslide causative factors and individual factor weight, Pij and Wj values of IOE model were estimated respectively to prepare landslide susceptibility map of Darjeeling Himalaya in GIS platform. The results suggested that the most important factor of landslide occurrences was soil type, whereas NDVI played least role in landslide susceptibility assessments. The prediction accuracy of the prepared landslide susceptibility map using ROC curve was 78.2%. Estimated frequency ratio value increased from very low to very high susceptibility zones, which showed the reliability and authenticity of IOE model.

Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression

Quantitative landslide susceptibility assessment is necessary for mitigating casualties, property damage, and economic loss. Identification of landslides and preparation of landslide susceptibility maps are crucial steps in landslide susceptibility assessment. Therefore, an optimal landslide susceptibility model is presented that is capable of producing accurate landslide susceptibility maps and assessing landslide susceptibility. To construct the optimal landslide susceptibility model, the effectiveness of the improved self-organizing linear output map (ISOLO), support vector machines (SVM) with four kernel functions (LN-SVM, PL-SVM, RBF-SVM, and SIG-SVM) and logistic regression (LR) was compared. Twelve landslide causative factors (namely, slope, slope aspect, elevation, curvature, profile curvature, plan curvature, slope length, topographic wetness index, distance to river, distance to road, distance to fault and annual maximum 24- and 48-h rainfalls) were used in this landslide susceptibility analysis. These models were applied to the Kaoping River basin in Southwestern Taiwan to assess its performance. Landslide inventory maps from 2008 to 2011 were collected. Data from the first three-year period were used for training and the remaining data was used for testing. The performance of the models was compared using accuracy and the area under the receiver operating characteristic curve as criteria. The results show that the RBF-SVM model outperformed the logistic regression in the study area. Using the RBF-SVM model, the landslide susceptibility under the annual 48-h maximum rainfall of various return periods were analyzed to assist local administrations and decision makers in disaster planning.

Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya

The present study aims to prepare landslide susceptibility map using frequency ratio (FR) model in the Balason river basin of Darjeeling Himalaya. Along with keeping in mind that, can the produced landslide susceptibility map yields acceptable landslide prediction accuracy or not. For that reliable landslide inventory map was prepared with a total of 295 landslide locations. The chosen landslide conditioning factors were altitude, aspect, slope angle, curvature, geology, geomorphology, soil, land use/land cover, normalized differential vegetation index, drainage density, lineament density, distance from lineament, distance to drainage, stream power index and topographic wetted index. To estimate FR value for each class of all the landslide conditioning factors pixels affected by landslide (%) and total pixels (%) were taken into account. FR model was applied to integrate all the data layers on GIS platform. The derived susceptibility map was divided into five categories i.e. very low, low moderate, high and very high which cover 17.52, 18.71, 30.35, 24.49 and 8.92% area of the basin respectively. The area under curve value of receiver operating characteristics indicates the prediction accuracy of the prepared map was 94.2% that is highly desirable. FR plots represent that there were positive relationship between landslide susceptibility classes and FR value. The prepared map will helping developers and planners to implement slope management plans, land use plans and other development action plans in this area.

GIS-based landslide susceptibility assessment: a case study in Kelemen Valley (Yenice—Karabuk, NW Turkey)

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.