Landslide Susceptibility Index Value Research Articles

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The resultsof this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

Geoenvironmental conditioning of landsliding in river valleys of lowland regions and its significance in landslide susceptibility assessment: A case study in the Lower Vistula Valley, Northern Poland

This paper presents an attempt to analyse landslide susceptibility of the Vistula River Valley in lowland Poland within young morainic areas. Although morphological and geological conditions of Northern European Lowland areas are different from those of mountainous and hilly areas, both of which are highly prone to mass movements, the frequency of landslides may also be very high on the slopes of large, entrenched river valleys. The frequency ratio (FR) and predictor rate (PR) methods were chosen to calculate the landslide susceptibility index (LSI) for entire landslides and detachment zones. Quantitative evaluation of the susceptibility model was performed by calculating area under the curve (AUC) for success rate curves (SRCs) and prediction rate curves (PRCs). To assign LSI values to individual classes the equal areas and the natural breaks methods were used. Identification and prioritization of causal factors show that the topographic attributes such as terrain ruggedness index (TRI), topographic wetness index (TWI), topographic position index (TPI) play a particularly important role. Most landslides develop on the slopes with a complex geological layering (i.e. two-layer or multi-layer setting) and those where the permeable layer (sand) separates less permeable layers (glacial till, clay/silt). In addition, landslides activate more frequently on slopes lacking erosional gullies. 81 % of unstable areas are on slopes with a small amount of erosional gullies, and the number of landslides decreases on slopes with a large number of erosional gullies. The selection of the study area turned out to be an important methodological issue; its geomorphological-geological homogeneity and low variability of the landslide types may have contributed to the very high values (AUC > 95) of SRC and PRC curves.

Developing a Landslide Susceptibility Map Using the Analytic Hierarchical Process in Ta Van and Hau Thao Communes, Sapa, Vietnam

Landslides are considered one of the most serious problems in the mountainous regions of the northern part of Vietnam due to the special topographic and geological conditions associated with the occurrence of tropical storms, steep slopes on hillsides, and human activities. This study initially identified areas susceptible to landslides in Ta Van Commune, Sapa District, Lao Cai Region using Analytical Hierarchy Analysis. Ten triggering and conditioning parameters were analyzed: elevation, slope, aspect, lithology, valley depth, relief amplitude, distance to roads, distance to faults, land use, and precipitation. The consistency index (CI) was 0.0995, indicating that no inconsistency in the decision-making process was detected during computation. The consistency ratio (CR) was computed for all factors and their classes were less than 0.1. The landslide susceptibility index (LSI) was computed and reclassified into five categories: very low, low, moderate, high, and very high. Approximately 9.9% of the whole area would be prone to landslide occurrence when the LSI value indicated at very high and high landslide susceptibility. The area under curve (AUC) of 0.75 illustrated that the used model provided good results for landslide susceptibility mapping in the study area. The results revealed that the predicted susceptibility levels were in good agreement with past landslides. The output also illustrated a gradual decrease in the density of landslide from the very high to the very low susceptible regions, which showed a considerable separation in the density values. Among the five classes, the highest landslide density of 0.01274 belonged to the very high susceptibility zone, followed by 0.00272 for the high susceptibility zone. The landslide susceptibility map presented in this paper would help local authorities adequately plan their landslide management process, especially in the very high and high susceptible zones.

Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.

Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India

Multivariate binary logistic regression (LR) model was used for the assessment of landslide susceptibility in the Rorachu river basin of eastern Sikkim Himalaya. For this purpose, a spatial database of 13 factors such as rainfall, slope, aspect, curvature, relief, drainage density, distance from drainage, distance from lineament, distance from road, geology, soil, Normalized Difference Vegetation Index (NDVI), and land use/land cover was constructed under Geographical Information System (GIS) environment. A landslide inventory map was prepared and converted into binary raster coded by 0 for absence and 1 for the presence of landslide. Total 946 landslide pixels were found out of which 725 landslide pixels (76.63%) were used as training dataset for the model and the model was validated using all landslide pixels. The coefficient value of geology was maximum followed by NDVI, soil and land use/land. The calculated probability value was used as Landslide Susceptibility Index (LSI). On the basis of the LSI value, the landslide susceptibility map was divided into five distinct categories of very low, low, moderate, high and very high susceptibility. The susceptibility classes depict that, 9.18% of total area was under very high and high susceptibility contains 75.48% area of total landslide. Finally the accuracy of the model was assessed by area under curve (AUC) of receiver operating characteristics (ROC) curve and landslide density method. The AUC value of 0.947 indicates a very good quality of the model and landslide density shows that the density is increasing with LSI classes.

Landslide susceptibility mapping in eastern hills of Rara Lake, western Nepal

The tendency of occurrences of slope failures in future in an area is landslides susceptibility. This tendency in eastern hills of Rara Lake was analyzed through mapping process. Bivariate statistical index method was used to produce the susceptibility of landslides. Ninety six slope failures were delineated from Google Earth imagery. The ratio of landslide densities of each individual factor classes to that of whole area gave weight values necessary to produce landslide susceptibility index (LSI). East and South facing slopes, gradients of <30o, elevation of 2000-2800 m, buffers closer to road and streams, grassland and cultivation lands, and lithology of pelitic metamorphic rocks were factor classes with positive weight values. The LSI values ranging from -7.12 to 5.45 were reclassified into five susceptibility classes. Landslide densities of 8.12 and 4.76 per km2 were observed for very high and high susceptible zones. Success rate curve, made from 35 landslides located in the field survey, showed 0.76 portion area under the curve. This gives 76% overall success rate. Higher susceptible areas are cultivated areas and grasslands, where most houses were located. Thus, knowing the landslide susceptibility of areas, necessary preparedness can be done to reduce the impacts.

Monitoring forest dynamics and landslide susceptibility in Mechi–Balason interfluves of Darjiling Himalaya, West Bengal using forest canopy density model (FCDM) and Landslide Susceptibility Index model (LSIM)

Mapping and monitoring of forest extent is a common requirement of regional forest inventories and public land natural resource management in India. The present study involves the assessment of forest dynamicity using forest canopy density model and the preparation of Landslide Susceptibility Index map using Landslide Susceptibility Index model in Mechi–Balason interfluves of Mirik Block of Darjiling Himalaya to find out the relationship between areal coverage of the forest and landslides. The forest canopy density model and forest pattern map of the study was prepared considering different forest parameter such as Bareness Index, Scaled Shadow Index, NDVI, fractional vegetation cover and forest canopy density on GIS platform. All these parameter were integrated applying Multi Criteria Decision Making Approach and Rank Sum Method to develop regeneration, degeneration and unchanged forest area map of the study area. Finally this forest map was incorporated with landslide susceptibility value to draw a conclusion about the role of forest cover in landslide. It is found that the Landslide Susceptibility Index value is higher in the degenerated portion of the forest and LSI is lower in the regenerated portion of the forest. So it can be concluded that landslide can be one of the major factor of forest dynamicity apart from this the other natural and anthropogenic factors may responsible for the forest dynamicity of the study area.

GIS based Grid overlay method versus modeling approach – A comparative study for landslide hazard zonation (LHZ) in Meta Robi District of West Showa Zone in Ethiopia

The present study area is located in Meta Robi District of West Showa Zone in Oromiya Regional State in Ethiopia. The main objective of the present study was to evaluate landslide hazard zonation (LHZ) by utilizing ‘Grid overlay’ and ‘GIS modeling’ approaches. Also, it was attempted to know the effectiveness of the two methods. The methodology followed was based on the analysis of past landslides in the area. For the present study six causative factors namely; slope material, slope, aspect, elevation, land use and land cover and groundwater surface traces were considered. Later, Landslide Susceptibility Index (LSI) was computed based on the relative influence of causative factors on past landslides. For the ‘Grid overlay’ method a grid with cells 10m by 10m was overlaid over the study area and later it was geo-processed to delineate various sub-classes of each causative factor. LSI values were assigned to each sub-causative factor within each grid cell and a ‘Total Landslide Susceptibility Index’ was calculated to produce the LHZ map. For ‘GIS modeling’ the same causative factors and similar LSI values were utilized. In the case of LHZ map prepared by the ‘Grid overlay’ method about 82% of past landslides fall within ‘very high hazard’ or ‘high hazard’ zones whereas in the case of ‘GIS modeling’ about 95% of past landslides fall within ‘very high hazard’ or ‘high hazard’ zones. Finally, the validation showed that ‘GIS modeling’ produced better LHZ map. Also, ‘Grid overlay’ method is more tedious and time consuming as compared to GIS modeling.

Integrating the Analytical Hierarchy Process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya

To prepare a landslide susceptibility map of Shiv-khola watershed, one of the landslide prone parts of Darjeeling Himalaya, remote sensing and GIS tools were used to integrate 10 landslide triggering parameters: lithology, slope angle, slope aspect, slope curvature, drainage density, upslope contributing area (UCA), lineament, settlement density, road contributing area (RCA), and land use and land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to derive factor weights using MATLAB with reasonable consistency ratio (CR). The frequency ratio (FR) model was used to derive class frequency ratio or class weights that indicate the relative importance of individual classes for each factor. The weighted linear combination (WLC) method was used to determine the landslide susceptibility index value (LSIV) on a GIS platform, by incorporating both factor weights and class weights. The Shiv-khola watershed is classified into five landslide susceptibility zones. The overall classification accuracy is 99.22 and Kappa Statistics is 0.894.

Synergistic application of fuzzy logic and geo-informatics for landslide vulnerability zonation—a case study in Sikkim Himalayas, India

The present study comprises the application of various Fuzzy algebraic functions for assessment of landslide vulnerability in Rumtek-Samdung area of Sikkim, which is a Himalayan state in India. The thematic layers for probable causative factors for landslides were collected from various spatial data sources. The main causative factors identified include pedologic factors viz. soil depth, soil texture, soil drainage behavior, soil stoniness, soil erosion and soil hydraulic conductivity, geologic factors such as lithology and foliation along with land use, slope, existence of road and drainage, etc. Landslide spots for the past few years were identified and detected using Cartosat panchromatic image of 2.5 m resolution and further augmented with Google image and field verification. A relation between the occurrence of landslides and each sub-category of the causative factors was established through their frequency ratio and converted to fuzzy membership values. In addition to the five Fuzzy operators used in previous researches, we have introduced two new operators named as Fuzzy AVERAGE(AR) which is computed as the average of AND, and OR operators, and Fuzzy AVERAGE(SP) which is computed as the average of Fuzzy algebraic SUM and Fuzzy algebraic PRODUCT operators. Landslide Susceptibility Index values were computed employing various Fuzzy operators based on which zonation maps were generated classifying the study area into five zones such as least vulnerable zone, low vulnerable zone, moderate vulnerable zone, highly vulnerable zone and most vulnerable zone. Vulnerability assessment accuracy was then computed based on the occurrences of past landslides in the higher three vulnerability zones. The Fuzzy GAMMA Operator exhibited the highest vulnerability assessment accuracy at Lambda = 0.5. The two newly formulated Fuzzy operators showed significant improvement in the vulnerability assessment accuracy in comparison to the two conventional Fuzzy operators’ namely Fuzzy algebraic PRODUCT and Fuzzy algebraic AND operator. Performance Index for the Fuzzy Operators is computed as the ratio of vulnerability assessment accuracy to the percentage of area in the higher three vulnerability zones. All the three newly formulated Fuzzy Average operators exhibited higher Performance Index compared to Fuzzy OR- and Fuzzy SUM-based operators indicating higher proficiency in landslide vulnerability assessment

Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models

The main objective of this study is to evaluate and compare the results of evidential belief functions and fuzzy logic models for spatial prediction of landslide hazards in the Hoa Binh province of Vietnam, using geographic information systems. First, a landslide inventory map showing the locations of 118 landslides that have occurred during the last ten years was constructed using data from various sources. Then, the landslide inventory was randomly partitioned into training and validation datasets (70% of the known landslide locations were used for training and building the landslide models and the remaining 30% for the model validation). Secondly, nine landslide conditioning factors were selected (i.e., slope, aspect, relief amplitude, lithology, landuse, soil type, distance to roads, distance to rivers and distance to faults). Using these factors, landslide susceptibility index values were calculated using evidential belief functions and fuzzy logic models. Finally, landslide susceptibility maps were validated and compared using the validation dataset that was not used in the model building. The prediction-rate curves and area under the curves were calculated to assess prediction capability. The results show that all the models have good prediction capabilities. The model derived using evidential belief functions has the highest prediction capability. The model derived using fuzzy SUM has the lowest prediction capability. The fuzzy PRODUCT and fuzzy GAMMA models have almost the same prediction capabilities. In general, all the models yield reasonable results that may be used for preliminary landuse planning purposes.