scholarly journals Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in Kaski District, Nepal

2021 ◽  
Vol 11 (1) ◽  
pp. 167-177
Author(s):  
Niraj Baral ◽  
Akhilesh Kumar Karna ◽  
Suraj Gautam
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Satellite Image ◽  
Image Interpretation ◽  
Google Earth ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Prediction Rate ◽  
Using Data

Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.

scholarly journals Landslide Susceptibility Mapping Using GIS-based Information Value and Frequency Ratio Methods in Gindeberet area, West Shewa Zone, Oromia Region, Ethiopia

2021 ◽  
Author(s):  
Alembante Genene ◽  
Matebie Meten
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Image Interpretation ◽  
Active Surface ◽  
Google Earth ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect

Abstract The study area is found in Gindeberet district of West Shewa zone in Oromia Regional State of Ethiopia.This area is highly susceptible to active surface processes due to the presence of rugged morphology with steep scarps, sharp ridges, cliffs, deep gorges and valleys. This study aimed to identify and evaluate the causative factors and to prepare the landslide susceptibility maps (LSMs) of the study area. Two bivariate statistical models i.e. Information value(IV) and the Frequency ratio(FR), were used. First, active, reactivated and passive landslides and scarps were identified using Google Earth image interpretation and extensive field survey for landslide inventory. A total of 580 landslide were randomly selected into two datasets in which (80%)460 landslides were used for modeling and (20%)116 landslidesfor validation. conditioning factors (slope, aspect, curvature, distance from stream, distance from lineaments, lithology, rainfall and land use) were combined with a training landslide dataset in a ArcGIS to generate LSMs which weredivided into verylow, low, moderate, high and veryhigh susceptibility zones. LSMs for IV and FR models were validated using the Area under(ROC) curve showing a success rate of 0.836 and 0.835 respectively and a predictive rate of 0.817 and 0.818 respectively wich showed a good performance of both models. The resulting LSMs can be used for land use planning and management.

scholarly journals Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Florence Elfriede Sinthauli Silalahi ◽  
Pamela ◽  
Yukni Arifianti ◽  
Fahrul Hidayat
Keyword(s):  
Land Cover ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Slope Aspect ◽  
Gis Mapping ◽  
Susceptibility Map ◽  
Ratio Model ◽  
Conditioning Factors ◽  
Landslide Susceptibility Index ◽  
Frequency Ratio Model

Abstract Landslides are common natural disasters in Bogor, Indonesia, triggered by a combination of factors including slope aspect, soil type and bedrock lithology, land cover and land use, and hydrologic conditions. In the Bogor area, slopes with volcanic lithologies are more susceptible to failure. GIS mapping and analysis using a Frequency Ratio Model was implemented in this study to assess the contribution of conditioning factors to landslides, and to produce a landslide susceptibility map of the study area. A landslide inventory map was prepared from a database of historic landslides events. In addition, thematic maps (soil, rainfall, land cover, and geology map) and Digital Elevation Model (DEM) were prepared to examine landslide conditioning factors. A total of 173 landslides points were mapped in the area and randomly subdivided into a training set (70%) with 116 points and test set with 57 points (30%). The relationship between landslides and conditioning factors was statistically evaluated with FR analysis. The result shows that lithology, soil, and land cover are the most important factors generating landslides. FR values were used to produce the Landslide Susceptibility Index (LSI) and the study area was divided into five zones of relative landslide susceptibility. The result of landslide susceptibility from the mid-region area of Bogor to the southern part was categorized as moderate to high landslide susceptibility zones. The results of the analysis have been validated by calculating the Area Under a Curve (AUC), which shows an accuracy of success rate of 90.10% and an accuracy of prediction rate curve of 87.30%, which indicates a high-quality susceptibility map obtained from the FR model.

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

scholarly journals Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

2019 ◽  
Vol 58 ◽  
pp. 163-171 ◽  
Author(s):  
Arishma Gadtaula ◽  
Subodh Dhakal
Keyword(s):  
Landslide Susceptibility ◽  
Google Earth ◽  
Weight Of Evidence ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Mountainous Area ◽  
High Susceptibility ◽  
Seismic Landslides ◽  
2015 Gorkha Earthquake ◽  
Very High

The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.

scholarly journals Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal)

2004 ◽  
Vol 4 (1) ◽  
pp. 133-146 ◽  
Author(s):  
J. L. Zêzere ◽  
E. Reis ◽  
R. Garcia ◽  
S. Oliveira ◽  
M. L. Rodrigues ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Hazard ◽  
Slope Instability ◽  
Rate Curve ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Return Periods ◽  
Data Set ◽  
Prediction Rate ◽  
Definition Of

Abstract. A general methodology for the probabilistic evaluation of landslide hazard is applied, taking in account both the landslide susceptibility and the instability triggering factors, mainly rainfall. The method is applied in the Fanhões-Trancão test site (north of Lisbon, Portugal) where 100 shallow translational slides were mapped and integrated into a GIS database. For the landslide susceptibility assessment it is assumed that future landslides can be predicted by statistical relationships between past landslides and the spatial data set of the predisposing factors (slope angle, slope aspect, transversal slope profile, lithology, superficial deposits, geomorphology, and land use). Susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Bayesian model) over unique-condition terrain units in a raster basis. The landslide susceptibility map is prepared by sorting all pixels according to the pixel susceptibility value in descending order. In order to validate the results of the susceptibility ana- lysis, the landslide data set is divided in two parts, using a temporal criterion. The first subset is used for obtaining a prediction image and the second subset is compared with the prediction results for validation. The obtained prediction-rate curve is used for the quantitative interpretation of the initial susceptibility map. Landslides in the study area are triggered by rainfall. The integration of triggering information in hazard assessment includes (i) the definition of thresholds of rainfall (quantity-duration) responsible for past landslide events; (ii) the calculation of the relevant return periods; (iii) the assumption that the same rainfall patterns (quantity/duration) which produced slope instability in the past will produce the same effects in the future (i.e. same types of landslides and same total affected area). The landslide hazard is present as the probability of each pixel to be affected by a slope movement, and results from the coupling between the susceptibility map, the prediction-rate curve, and the return periods of critical rainfall events, on a scenario basis. Using this methodology, different hazard scenarios were assessed, corresponding to different rain paths with different return periods.

scholarly journals Landslide Susceptibility Mapping using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

2021 ◽  
Author(s):  
Md. Sharafat Chowdhury ◽  
Bibi Hafsa
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Information Value ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Conditioning Factors

Abstract This study attempts to produce Landslide Susceptibility Map for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). A secondary landslide inventory database was used to correlate the previous landslides with the landslide conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.

scholarly journals LANDSLIDE SUSCEPTIBILITY MAPPING IN THE MUNICIPALITY OF OUDKA, NORTHERN MOROCCO: A COMPARISON BETWEEN LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 41-49 ◽  
Author(s):  
S. Benchelha ◽  
H. Chennaoui Aoudjehane ◽  
M. Hakdaoui ◽  
R. El Hamdouni ◽  
H. Mansouri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Landslide Susceptibility ◽  
Model Building ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Prediction Rate ◽  
Artificial Neural

<p><strong>Abstract.</strong> The Rif is among the areas of Morocco most susceptible to landslides, because of the existence of relatively young reliefs marked by a very important dynamics compared to other regions. These landslides are one of the most serious problems on many levels: social, economic and environmental. The increase in the frequency and impact of landslides over the past decade has demonstrated the need for an in-depth study of these phenomena, allowing the identification of areas susceptible to landslides.</p><p> The main objective of this study is to identify the optimal method for the mapping of the area susceptible to landslides in municipality of Oudka. This area has been marked by the largest landslide in the region, caused by heavy rainfall in 2013. Two Statistical Methods i) Regression Logistics (LR) ii) Artificial Neural Networks (ANN), were used to create a landslide susceptibility map. The realization of this susceptibility map required, first, the mapping of old landslides by the aerial photography, the data of the geological map and by the data obtained using field surveys using GPS. A total of 105 landslides were mapped from these various sources. 50% of this database was used for model building and 50% for validation. Eight independent landslide factors are exploited to detect the most sensitive areas: altitude, slope, aspect, distance of faults, distance streams, distance from roads, lithology and vegetation index (NDVI).</p><p> The results of the landslide susceptibility analysis were verified using success and prediction rates. The success rate (AUC&amp;thinsp;=&amp;thinsp;0.918) and the prediction rate (AUC&amp;thinsp;=&amp;thinsp;0.901) of the LR model is higher than that of the ANN model (success rate (AUC&amp;thinsp;=&amp;thinsp;0.886) and prediction rate (AUC&amp;thinsp;=&amp;thinsp;0.877).</p><p> These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.</p>

scholarly journals A new approach in the development and analysis of the landslide susceptibility map of the hillslopes of Bujumbura, Burundi

2021 ◽  
pp. 26-34
Author(s):  
Desire Kubwimana ◽  
Lahsen Ait Brahim ◽  
Abdellah Abdelouafi
Keyword(s):  
Landslide Susceptibility ◽  
Slope Angle ◽  
Landslide Inventory ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Aerial Photos ◽  
Conditioning Factors ◽  
Mountainous Regions ◽  
Tropical Environments

As in other hilly and mountainous regions of the world, the hillslopes of Bujumbura are prone to landslides. In this area, landslides impact human lives and infrastructures. Despite the high landslide-induced damages, slope instabilities are less investigated. The aim of this research is to assess the landslide susceptibility using a probabilistic/statistical data modeling approach for predicting the initiation of future landslides. A spatial landslide inventory with their physical characteristics through interpretation of high-resolution optic imageries/aerial photos and intensive fieldwork are carried out. Base on in-depth field knowledge and green literature, let’s select potential landslide conditioning factors. A landslide inventory map with 568 landslides is produced. Out of the total of 568 landslide sites, 50 % of the data taken before the 2000s is used for training and the remaining 50 % (post-2000 events) were used for validation purposes. A landslide susceptibility map with an efficiency of 76 % to predict future slope failures is generated. The main landslides controlling factors in ascendant order are the density of drainage networks, the land use/cover, the lithology, the fault density, the slope angle, the curvature, the elevation, and the slope aspect. The causes of landslides support former regional studies which state that in the region, landslides are related to the geology with the high rapid weathering process in tropical environments, topography, and geodynamics. The susceptibility map will be a powerful decision-making tool for drawing up appropriate development plans in the hillslopes of Bujumbura with high demographic exposure. Such an approach will make it possible to mitigate the socio-economic impacts due to these land instabilities

scholarly journals Evaluating landslide susceptibility using environmental factors, fuzzy membership functions and GIS

2013 ◽  
Vol 13 (1) ◽  
pp. 28-40
Keyword(s):  
Factor Analysis ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Fuzzy Membership ◽  
Susceptibility Map ◽  
Membership Functions ◽  
Conditioning Factors ◽  
Fuzzy Membership Functions

A methodology for landslide susceptibility assessment to delineate landslide prone areas is presented using factor analysis and fuzzy membership functions and Geographic Information Systems (GIS). A landslide inventory of 51 landslides was created in the mountainous part of Xanthi prefecture (North Greece) and the associated conditioning factors were determined for each landslide by field work. Six conditioning factors were evaluated: slope angle, slope aspect, land use, geology, distance to faults and topographical elevation. Fuzzy membership functions were defined for each factor using the landslide frequency data. Factor analysis provided weights (i.e., importance for landslide occurrences) for each one of the above conditioning factors, indicating the most important factors as geology and slope angle. An overlay and index method was adopted to produce the landslide susceptibility map. In this map 96% of the observed landslides are located in very high and high susceptibility zones, indicating a suitable approach for landslide susceptibility mapping.

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