Landslide susceptibility along NH-39 between Karong and Mao,Senapati district,Manipur

National Highway-39 is the only lifeline of the state of Manipur. The sector between Karong and Mao is highly landslide prone and frequently blocked the NH-39. The area is represented by Disang and Barail Groups of rock. Considering the importance of NH-39, landslide susceptibility zonation studies along NH-39 between Karong and Mao were carried out following GSI guideline, a modified form of BIS (1998).     

Landslide susceptibility mapping based on frequency ratio and logistic regression models

The aim of this study is to apply and compare a probability model, frequency ratio and statistical model, and a logistic regression to Sajaroud area, Northern Iran using geographic information system. Landslide locations of the study area were detected from interpretation of aerial photographs and field surveys. Landslide-related factors such as elevation, slope gradient, slope aspect, slope curvature, rainfall, distance to fault, distance to drainage, distance to road, land use, and geology were calculated from the topographic and geology map and LANDSAT ETM satellite imagery. The spatial relationships between the landslide location and each landslide-related factor were analyzed and then landslide susceptibility maps were produced using the frequency ratio and forward stepwise logistic regression methods. Finally, the maps were tested and compared using known landslide locations, and success rates were calculated. Predicted accuracy values for frequency ratio (79.48%) and logistic regression models showed that the map obtained from frequency ratio model is more accurate than the logistic regression (77.4%) model. The models used in this study have shown a great deal of importance for watershed management and land use planning.     

Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models

This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.     

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

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

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.     

Landslide susceptibility mapping of Tevankarai Ar sub-watershed,Kodaikkanal taluk,India, using weighted similar choice fuzzy model

This paper deals with the landslide susceptibility zonation of Tevankarai Ar sub-watershed using weighted similar choice fuzzy method in a GIS environment. There has been a rapid increase in landslide occurrences in the Kodaikkanal town and area surrounding the town specially in the settlements around the town and road links leading to and from the town. This necessitates a detailed study of slope instability problems in this area. It is observed that these incidences occur frequently during the monsoon and summer showers. Rainfall is identified as the prime triggering factor. Eleven physical factors that cause instability are identified as causative factors from the field investigations and landslide occurrences. Land use pattern, slope gradient, curvature and aspect, weathering index which are evaluated from the weathering ratios of different chemical constituents of the three major lithological variations, soil type, hydraulic conductivity of soil and soil thickness, geomorphology, drainage, and lineament have been utilized to prepare the spatial variation. A weighted similar choice fuzzy model which ranks a set of alternatives by identifying the similarity between the outcome of alternatives and outcome of ideal alternatives is used to rank the causative factors. Each causative factor is classified into sub-categories and rated based on their effect on stimulating the landslide event using qualitative judgment derived from field studies and landslide history. The prepared thematic maps of causative factors are integrated, utilizing the GIS software Arcmap. The outcome has projected the low, moderate, high, and very high landslide susceptibility zones. The high-hazard and very high-hazard areas fall in the northwestern part characterized by croplands and agricultural plantations, while the moderate hazard zones are seen in prominent settlements and low-hazard zones are observed in the sparse settlements and zones of less agricultural activity. The model is verified using the relative landslide density (R) index, and the susceptibility map is found to be consistent with the mapped landslide incidences. The results from this study illustrate that the use of weighted similar choice fuzzy method is suitable for landslide susceptibility mapping on regional scale in growing hill towns as Kodaikkanal town.     

Landslide susceptibility mapping at Zonouz Plain,Iran using genetic programming and comparison with frequency ratio,logistic regression,and artificial neural network models

Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.     

Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region,northwest of Iran

Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.     

Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province,Iran

GeoJournal - Landslides are natural destructive phenomena that can cause great damage to property and life loss. One of the fundamental proceedings to reduce the possible damage is identifying...     

Landslide hazard mapping along national highway-154A in Himachal Pradesh,India using information value and frequency ratio

For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area.     

Landslide susceptibility assessment of national highway 1D from Sonamarg to Kargil,Jammu and Kashmir,India using frequency ratio method

GeoJournal - This study is an attempt to carry landslide susceptibility assessment along national highway 1D a lifeline to the Ladakh region using Frequency Ratio Method in GIS environment. The...     

Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India

This research paper assesses the vulnerability of landslide for the Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India, using remotely sensed data and geographic information system (GIS). Landslide database was generated using IRS-1C satellite LISS III data and aerial photographs accompanied by field investigations using differential global positioning system to generate a landslide inventory map. Topographical, spatial, and field data were processed to construct the spatial thematic layers using image processing and GIS environment. Twelve landslide-inducing factors were used for landslide vulnerability analysis: elevation, slope, aspect, plan curvature, profile curvature, proximity to road, drainage and lineament, land use/land cover, geology, geomorphology, and run-off. The first five factors were derived from digital elevation model, and other thematic layers were prepared from spatial database. Frequency ratio of each factor was computed using the above thematic factors with past landslide locations. Landslide vulnerability map was produced using raster analysis. The landslide vulnerability map was classified into five zones: very low, low, moderate, high, and very high. The model is validated using the relative landslide density index (R-index method). The consistency of R-index indicates good performance of the vulnerability map.     

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco,Italy

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

     

Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).     

Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: a case study from Garuwa sub-basin,East Nepal

For assessing landslide susceptibility, the spatial distribution of landslides in the field is essential. The landslide inventory map is prepared on the basis of historical information of individual landslide events from different sources such as previously published reports, satellite imageries, aerial photographs and interview with local inhabitants. Then, the distribution of landslides in the study area is verified with field surveys. However, the selection of contributing factors for modelling landslide susceptibility is an inhibit task. The previous studies show that the factors are chosen as per availability of data. This paper documents the landslide susceptibility mapping in the Garuwa sub-basin, East Nepal using frequency ratio method. Nine different contributing factors are considered: slope aspect, slope angle, slope shape, relative relief, geology, distance from faults, land use, distance from drainage and annual rainfall. To analyse the effect of contributing factors, the landslide susceptibility index maps are generated four times using (a) topographical factors and geological factors, (b) topographical factors, geological factors and land use, (c) topographical factors, geological factors, land use and drainage and (d) all nine causative factors. By comparing with the pre-existing landslides, the fourth case (considering all nine causative factors) yields the best success rate accuracy, i.e. 81.19 %, which is then used to produce the final landslide susceptibility zonation map. Then, the final landslide susceptibility map is validated through chi-square test. The standard chi-square value with 3 degrees of freedom at the 0.001 significance level is 16.3, whereas the calculated chi-square value is 7,125.79. Since the calculated chi-square value is greater than the standard chi-square value, it can be concluded that the landslide susceptibility map is considered as statistically significant. Moreover, the results show that the predicted susceptibility levels are found to be in good agreement with the past landslide occurrences.     

Landslide susceptibility mapping by using fuzzy logic: a case study of Cham-gardalan catchment,Ilam, Iran

Landslides are introduced as regional movements, which influence different engineering structures such as roads, railways, and dams and cause the person’s death. Identification of landslide zones may decrease the financial losses and human injuries or deaths. This study tries to achieve a landslide susceptibility mapping in Cham-gardalan catchment by weighting the main criteria and the membership functions of fuzzy logic. For this, we applied the best relationship function between the presence and absence of landslides as well as a collection of the elements. At first, the landslide points were identified by the means of some components those of satellite images, topographical (1:50,000) and geographical (1:100,000) maps, field visits, and Google Earth software followed by the preparation of landslide distribution maps. Then, all effective landslide factors such as percentage of slope, slope aspect, height, geology, land uses, distance from roads, distance from drainages, distance from breakage, and precipitation map have been utilized in order to conduct the fuzzy analyses. Landslide susceptibility map was performed by fuzzy operators (Gamma, Product, Sum, Or, And) in the study area. After fuzzificating and weighting, the effective criteria of landslides were determined through fuzzy Gamma operators with the landaus of 0.2, 0.5, 0.8, and 0.9 and by comparing final maps for making an appropriate model of landslide susceptibility mapping. The regional susceptibility map represents the landslide-prone areas in five categories those of very low, low, moderate, high, and very high. Our results indicated that among the applied operators, Gamma with landau of 0.9 can be used as an appropriate method for mapping the landslide susceptibility due to the suitable fuzzification of given criteria based on landslide distribution maps. In addition, the elements of road, percentage of slope, distance from drainage, and geology were recognized as the most important factors for occurring the landslides.     

Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model

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

Application of frequency ratio,statistical index,and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.