Analysis of factors affecting the Spatial Distribution of co-seismic landslides triggered by the 2011 (Mw 6.9) Sikkim earthquake

2021 ◽  
Author(s):  
Saurav Kumar ◽  
Sengupta Aniruddha
Keyword(s):  
Slope Failure ◽  
Slope Angle ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Past Study ◽  
Topographic Wetness Index ◽  
Landslide Occurrence ◽  
Seismic Landslides ◽  
Sikkim Earthquake ◽  
Seismic Landslide

<p>The Himalayan region is known as an earthquake-triggered landslides prone area. It is characterized by high seismicity, large relative relief, steep slopes, and dense precipitation. These seismically triggered landslides are likely to affect substantial societal impacts, including loss of life, damage to houses, public buildings, various lifeline structures like highways, railways tracks, etc. Further, they obstruct post-earthquake emergency response efforts. A past study by Martha et al. 2014 reported that an earthquake of Mw 6.9 in 2011 triggered 1196 landslides in Sikkim which is a part of the eastern Himalayas. The slope failure events are controlled by several factors, which can be grouped into four main classes: seismology, topography, lithology, and hydrology. Each class contains several sub-factors. Having in-depth knowledge of these factors and their influence on the density of landslide events in the affected area due to the 2011 Sikkim earthquake is essential to realize the level of threat of co-seismic landslide due to future earthquakes. Eight landslide controlling factors is considered in this analysis including peak ground acceleration (PGA), slope, aspect, elevation, curvature, lithology, distance from rivers, and topographic wetness index (TWI). Further, the frequency ratio model using the GIS framework is applied to evaluate the contribution of each landslide controlling factor to landslide occurrence. Scatter plots between the number of landslides per km<sup>2</sup> (LN) and percentage of landslide area (LA) and causative factors indicate that distance from the river, slope angle, and PGA are the dominant factors that control the landslides. The results of the above analysis showed that the majority of co-seismic landslides occurred at slope >30°, preferably in East, Southeast, and South directions and near river within a distance of 1500 m. The detailed study of interactions among these factors can improve the understanding of the mechanisms of co-seismic landslide occurrence in Sikkim and will be useful for producing a co-seismic landslide susceptibility map of the area.</p>

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 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 Landslides triggered by the 12 January 2010 Mw 7.0 Port-au-Prince, Haiti, earthquake: visual interpretation, inventory compiling and spatial distribution statistical analysis

2014 ◽  
Vol 2 (2) ◽  
pp. 1259-1331 ◽  
Author(s):  
C. Xu ◽  
J. B. H. Shyu ◽  
X.-W. Xu
Keyword(s):  
Spatial Distribution ◽  
Slope Angle ◽  
Slope Aspect ◽  
Haiti Earthquake ◽  
Number Density ◽  
Landslide Area ◽  
Seismic Landslides ◽  
Seismic Landslide ◽  
Area Percentage ◽  
Landslide Area Percentage

Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and their erosion thicknesses with topographic factors, seismic parameters, and their distance from roads. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolutions satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various landslide controlling parameters. These controlling parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons of any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to update on the basis of the abundant and more complete co-seismic landslide inventories recently available.

scholarly journals Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4636 ◽  
Author(s):  
Hossein Moayedi ◽  
Dieu Tien Bui ◽  
Loke Kok Foong
Keyword(s):  
Slope Stability ◽  
Slope Failure ◽  
Environmental Parameters ◽  
Slope Aspect ◽  
Remotely Sensed Data ◽  
Topographic Wetness Index ◽  
Invasive Weed ◽  
Landslide Occurrence ◽  
Stability Monitoring ◽  
Analytical Approaches

By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions. To this end, after training the proposed EHO-NF and IWO-NF ensembles using training landslide events, their generalization power is evaluated by receiving operating characteristic curves. The results demonstrated around 75% accuracy of prediction for both models; however, the IWO-NF achieved a better understanding of landslide distribution pattern. Due to the successful performance of the implemented models, they could be promising alternatives to mathematical and analytical approaches being used for discerning the relationship between the slope failure and environmental parameters.

scholarly journals Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, <i>M</i><sub>w</sub> = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis

2014 ◽  
Vol 14 (7) ◽  
pp. 1789-1818 ◽  
Author(s):  
C. Xu ◽  
J. B. H. Shyu ◽  
X. Xu
Keyword(s):  
Spatial Distribution ◽  
Slope Angle ◽  
Slope Aspect ◽  
Haiti Earthquake ◽  
Number Density ◽  
Landslide Area ◽  
Seismic Landslides ◽  
Seismic Landslide ◽  
Area Percentage ◽  
Landslide Area Percentage

Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw= 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and the thicknesses of their erosion with topographic, geologic, and seismic parameters. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolution satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various environmental parameters. These parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons for any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to be updated on the basis of the abundant and more complete co-seismic landslide inventories recently available.

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 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.

scholarly journals Large landslides at the northeastern margin of the Bayan Har Block, Tibetan Plateau, China

2019 ◽  
Vol 6 (1) ◽  
pp. 180844 ◽  
Author(s):  
Bo Zhao ◽  
Yunsheng Wang ◽  
Yonghong Luo ◽  
Ruifeng Liang ◽  
Jia Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Slope Angle ◽  
Slope Aspect ◽  
The Tibetan Plateau ◽  
Landslide Occurrence ◽  
Eastern Margin ◽  
Characteristics Analysis ◽  
Bayan Har

Large landslides (volume greater than or equal to 10 6 m 3 ) usually have disastrous consequences and clearly influence the evolution of the local landscape. In this study, a detailed investigation of large landslides, across 20 towns over an area of 5000 km 2 , was carried out on the northeastern margin of the Bayan Har Block, at the eastern margin of the Tibetan Plateau, China. The results show that there are 129 large landslides in this area. Among them, 79 landslides have volumes within 10 6 –10 7 m 3 , 52 landslides have volumes within 10 7 –10 8 m 3 and 2 landslides have volumes larger than 10 8 m 3 . Most of these landslides are distributed along rivers, and more than 32% are densely concentrated in three small regions. The landslides mainly occur in high slopes and exhibit obvious sturzstrom characteristics. Analysis of the factors controlling landslide occurrence shows that elevation, slope angle, slope aspect, lithology, faults and rivers (valley) clearly influence landslide occurrence, while rainfall has no obvious influence. Earthquakes are considered an important trigger of and contributor to landslide occurrence.

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