Search results for: landslide susceptibility

Authors: Y. H. Wu, Ji-Yuan Lin, Yu-Ming Liou

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This study aims to set up the landslide susceptibility map of County Route 89 at Ren-Ai Township in Nantou County using the Instability Index Method and Logistic regression. Seven susceptibility factors including Slope Angle, Aspect, Elevation, Distance to fold, Distance to River, Distance to Road and Accumulated Rainfall were obtained by GIS based on the Typhoon Toraji landslide area identified by Industrial Technology Research Institute in 2001. To calculate the landslide percentage of each factor and acquire the weight and grade the grid by means of Instability Index Method. In this study, landslide susceptibility can be classified into four grades: high, medium high, medium low and low, in order to determine the advantages and disadvantages of the two models. The precision of this model is verified by classification error matrix and SRC curve. These results suggest that the logistic regression model is a preferred method than instability index in the assessment of landslide susceptibility. It is suitable for the landslide prediction and precaution in this area in the future.

Keywords: instability index method, logistic regression, landslide susceptibility, SRC curve

Procedia PDF Downloads 260

Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

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The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

Procedia PDF Downloads 161

Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

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Authors: Gabriel Legorreta Paulin, Marcus I. Bursik, Lilia Arana Salinas, Fernando Aceves Quesada

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In volcanic environments, landslides and debris flows occur continually along stream systems of large stratovolcanoes. This is the case on Pico de Orizaba volcano, the highest mountain in Mexico. The volcano has a great potential to impact and damage human settlements and economic activities by landslides. People living along the lower valleys of Pico de Orizaba volcano are in continuous hazard by the coalescence of upstream landslide sediments that increased the destructive power of debris flows. These debris flows not only produce floods, but also cause the loss of lives and property. Although the importance of assessing such process, there is few landslide inventory maps and landslide susceptibility assessment. As a result in México, no landslide susceptibility models assessment has been conducted to evaluate advantage and disadvantage of models. In this study, a comprehensive study of landslide susceptibility models assessment using GIS technology is carried out on the SE flank of Pico de Orizaba volcano. A detailed multi-temporal landslide inventory map in the watershed is used as framework for the quantitative comparison of two landslide susceptibility maps. The maps are created based on 1) the Stability Index MAPping (SINMAP) model by using default geotechnical parameters and 2) by using findings of volcanic soils geotechnical proprieties obtained in the field. SINMAP combines the factor of safety derived from the infinite slope stability model with the theory of a hydrologic model to produce the susceptibility map. It has been claimed that SINMAP analysis is reasonably successful in defining areas that intuitively appear to be susceptible to landsliding in regions with sparse information. The validations of the resulting susceptibility maps are performed by comparing them with the inventory map under LOGISNET system which provides tools to compare by using a histogram and a contingency table. Results of the experiment allow for establishing how the individual models predict the landslide location, advantages, and limitations. The results also show that although the model tends to improve with the use of calibrated field data, the landslide susceptibility map does not perfectly represent existing landslides.

Keywords: GIS, landslide, modeling, LOGISNET, SINMAP

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Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

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Authors: Ismayanti Magfirah, Sartohadi Junun, Samodra Guruh

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Landslides are one of the most frequent and devastating natural disasters in Indonesia. Many studies have been conducted regarding this phenomenon. However, there is a lack of attention in the landslide inventory mapping. The natural condition (dense forest area) and the limited human and economic resources are some of the major problems in building landslide inventory in Indonesia. Considering the importance of landslide inventory data in susceptibility, hazard, and risk analysis, it is essential to generate landslide inventory based on available resources. In order to achieve this, the first thing we have to do is identify the landslides' location. The presence of Sentinel-1A and Sentinel-2A data gives new insights into land monitoring investigation. The free access, high spatial resolution, and short revisit time, make the data become one of the most trending open sources data used in landslide mapping. Sentinel-1A and Sentinel-2A data have been used broadly for landslide detection and landuse/landcover mapping. This study aims to generate landslide map by integrating Sentinel-1A and Sentinel-2A data use change detection method. The result will be validated by field investigation to make preliminary landslide inventory in the study area.

Keywords: change detection method, landslide inventory mapping, Sentinel-1A, Sentinel-2A

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Authors: Norbert Simon, Rodeano Roslee, Abdul Ghani Rafek, Goh Thian Lai, Azimah Hussein, Lee Khai Ern

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The 54 km stretch of Ranau-Tambunan (RTM) road in Sabah is subjected to slope failures almost every year. This study is focusing on identifying section of roads that are susceptible to failure based on temporal landslide density and lineament density analyses. In addition to the analyses, the rock slopes in several sections of the road were assessed using the geological strength index (GSI) technique. The analysis involved 148 landslides that were obtained in 1978, 1994, 2009 and 2011. The landslides were digitized as points and the point density was calculated based on every 1km2 of the road. The lineaments of the area was interpreted from Landsat 7 15m panchromatic band. The lineament density was later calculated based on every 1km2 of the area using similar technique with the slope failure density calculation. The landslide and lineament densities were classified into three different classes that indicate the level of susceptibility (low, moderate, high). Subsequently, the two density maps were overlap to produce the final susceptibility map. The combination of both high susceptibility classes from these maps signifies the high potential of slope failure in those locations in the future. The final susceptibility map indicates that there are 22 sections of the road that are highly susceptible. Seven rock slopes were assessed along the RTM road using the GSI technique. It was found from the assessment that rock slopes along this road are highly fractured, weathered and can be classified into fair to poor categories. The poor condition of the rock slope can be attributed to the high lineament density that presence in the study area. Six of the rock slopes are located in the high susceptibility zones. A detailed investigation on the 22 high susceptibility sections of the RTM road should be conducted due to their higher susceptibility to failure, in order to prevent untoward incident to road users in the future.

Keywords: GSI, landslide, landslide density, landslide susceptibility, lineament density

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Authors: Masood Beheshtirad

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Identification of regions having potential for landslide occurrence is one of the basic measures in natural resources management. Different landslide hazard mapping models are proposed based on the environmental condition and goals. In this research landslide hazard map using multiple regression model were provided and applicability of this model is investigated in Baghdasht watershed. Dependent variable is landslide inventory map and independent variables consist of information layers as Geology, slope, aspect, distance from river, distance from road, fault and land use. For doing this, existing landslides have been identified and an inventory map made. The landslide hazard map is based on the multiple regression provided. The level of similarity potential hazard classes and figures of this model were compared with the landslide inventory map in the SPSS environments. Results of research showed that there is a significant correlation between the potential hazard classes and figures with area of the landslides. The multiple regression model is suitable for application in the Baghdasht Watershed.

Keywords: landslide, mapping, multiple model, regression

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Authors: Kevin Potoczny, Katsuichiro Goda

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The St. Lawrence lowlands extend from Ottawa to Quebec City and are known for large deposits of sensitive Leda clay. Leda clay deposits are responsible for many large landslides, such as the 1993 Lemieux and 2010 St. Jude (4 fatalities) landslides. Due to the large extent and sensitivity of Leda clay, regional hazard analysis for landslides is an important tool in risk management. A 2018 regional study by Farzam et al. on the susceptibility of Leda clay slopes to landslide hazard uses 1 arc second topographical data. A qualitative method known as Hazus is used to estimate susceptibility by checking for various criteria in a location and determine a susceptibility rating on a scale of 0 (no susceptibility) to 10 (very high susceptibility). These criteria are slope angle, geological group, soil wetness, and distance from waterbodies. Given the flat nature of St. Lawrence lowlands, the current assessment fails to capture local slopes, such as the St. Jude site. Additionally, the data did not allow one to analyze failure planes accurately. This study majorly improves the analysis performed by Farzam et al. in two aspects. First, regional assessment with high resolution data allows for identification of local locations that may have been previously identified as low susceptibility. This then provides the opportunity to conduct a more refined analysis on the failure plane of the slope. Slopes derived from 1 arc second data are relatively gentle (0-10 degrees) across the region; however, the 1- and 2-meter resolution 2022 HRDEM provided by NRCAN shows that short, steep slopes are present. At a regional level, 1 arc second data can underestimate the susceptibility of short, steep slopes, which can be dangerous as Leda clay landslides behave retrogressively and travel upwards into flatter terrain. At the location of the St. Jude landslide, slope differences are significant. 1 arc second data shows a maximum slope of 12.80 degrees and a mean slope of 4.72 degrees, while the HRDEM data shows a maximum slope of 56.67 degrees and a mean slope of 10.72 degrees. This equates to a difference of three susceptibility levels when the soil is dry and one susceptibility level when wet. The use of GIS software is used to create a regional susceptibility map across the St. Lawrence lowlands at 1- and 2-meter resolutions. Failure planes are necessary to differentiate between small and large landslides, which have so far been ignored in regional analysis. Leda clay failures can only retrogress as far as their failure planes, so the regional analysis must be able to transition smoothly into a more robust local analysis. It is expected that slopes within the region, once previously assessed at low susceptibility scores, contain local areas of high susceptibility. The goal is to create opportunities for local failure plane analysis to be undertaken, which has not been possible before. Due to the low resolution of previous regional analyses, any slope near a waterbody could be considered hazardous. However, high-resolution regional analysis would allow for more precise determination of hazard sites.

Keywords: hazus, high-resolution DEM, leda clay, regional analysis, susceptibility

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Authors: Kumari Sweta, Ajanta Goswami, Abhilasha Dixit

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Landslide is a geomorphic process that plays an essential role in the evolution of the hill-slope and long-term landscape evolution. But its abrupt nature and the associated catastrophic forces of the process can have undesirable socio-economic impacts, like substantial economic losses, fatalities, ecosystem, geomorphologic and infrastructure disturbances. The estimated fatality rate is approximately 1person /100 sq. Km and the average economic loss is more than 550 crores/year in the Himalayan belt due to landslides. This study presents a comparative performance of a statistical bivariate method and a machine learning technique for landslide susceptibility mapping in and around Dharamshala, Himachal Pradesh. The final produced landslide susceptibility maps (LSMs) with better accuracy could be used for land-use planning to prevent future losses. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. A total of 209 landslide locations were identified in using high-resolution linear imaging self-scanning (LISS IV) data. The thematic maps of parameters influencing landslide occurrence were generated using remote sensing and other ancillary data in the GIS environment. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. LSMs were prepared using information value (Info Val), and Fuzzy Expert System (FES) models. Info Val is a statistical bivariate method, in which information values were calculated as the ratio of the landslide pixels per factor class (Si/Ni) to the total landslide pixel per parameter (S/N). Using this information values all parameters were reclassified and then summed in GIS to obtain the landslide susceptibility index (LSI) map. The FES method is a machine learning technique based on ‘mean and neighbour’ strategy for the construction of fuzzifier (input) and defuzzifier (output) membership function (MF) structure, and the FR method is used for formulating if-then rules. Two types of membership structures were utilized for membership function Bell-Gaussian (BG) and Trapezoidal-Triangular (TT). LSI for BG and TT were obtained applying membership function and if-then rules in MATLAB. The final LSMs were spatially and statistically validated. The validation results showed that in terms of accuracy, Info Val (83.4%) is better than BG (83.0%) and TT (82.6%), whereas, in terms of spatial distribution, BG is best. Hence, considering both statistical and spatial accuracy, BG is the most accurate one.

Keywords: bivariate statistical techniques, BG and TT membership structure, fuzzy expert system, information value method, machine learning technique

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Authors: Hilman Agil Satria, Rezky Naufan Hendrawan

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Indonesia is vulnerable to geological hazard because of its location in subduction zone and have tropical climate. Landslide is one of the most happened geological hazard in Indonesia, based on Indonesia Geospasial data, at least 194 landslides recorded in 2013. In fact, research location is placed as the third city that most happened landslide in Indonesia. Landslide caused damage of many houses and wrecked the road. The purpose of this research is to make a landslide zone therefore can be used as one of mitigation consideration. The location is in Bruno, Porworejo district Central Java Province Indonesia at 109.903 – 109.99 and -7.59 – -7.50 with 10 Km x 10 Km wide. Based on geological mapping result, the research location consist of Late Miocene sandstone and claystone, and Pleistocene volcanic breccia and tuff. Those landslide happened in the lithology that close with fault zone. This location has so many geological structures: joints, faults and folds. There are 3 thrust faults, 1 normal faults, 4 strike slip faults and 6 folds. This geological structure movement is interpreted as the main factor that has triggered landslide in this location. This research use field data as well as samples of rock, joint, slicken side and landslide location which is combined with DEM SRTM to analyze geomorphology. As the final result of combined data will be presented as geological map, geological structure map and landslide zone map. From this research we can assume that there is correlation between geological structure and landslide locations.

Keywords: geological structure, landslide, Porworejo, Indonesia

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Authors: Neetu Tyagi, Sumit Sharma

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The study illustrates the results of an integrated study done on Tangni landslide located on NH-58 at Chamoli, Uttarakhand. Geological, geo-morphological and geotechnical investigations were carried out to understand the mechanism of landslide and to plan further investigation and monitoring. At any rate, the movements were favored by continuous rainfall water infiltration from the zones where the phyllites/slates and Dolomites outcrop. The site investigations were carried out including the monitoring of landslide movements and of the water level fluctuations due to rainfall give us a better understanding of landslide dynamics that have been causing in time soil instability at Tangni landslide site. The Early Warning System (EWS) installed different types of sensors and all sensors were directly connected to data logger and raw data transfer to the Defence Terrain Research Laboratory (DTRL) server room with the help of File Transfer Protocol (FTP). The slip surfaces were found at depths ranging from 8 to 10 m from Geophysical survey and hence sensors were installed to the depth of 15m at various locations of landslide. Rainfall is the main triggering factor of landslide. In this study, the developed model of unsaturated soil slope stability is carried out. The analysis of sensors data available for one year, indicated the sliding surface of landslide at depth between 6 to 12m with total displacement up to 6cm per year recorded at the body of landslide. The aim of this study is to set the threshold and generate early warning. Local peoples already alert towards landslide, if they have any types of warning system.

Keywords: early warning system, file transfer protocol, geo-morphological, geotechnical, landslide

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Authors: Ankit Tyagi, Reet Kamal Tiwari, Naveen James

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In India, landslides are the most frequently occurring disaster in the regions of the Himalayas and the Western Ghats. The steep slopes and land use in these areas are quite apprehensive. In the recent past, many landslide hazard zonation (LHZ) works have been carried out in the Himalayas. However, the preparation of LHZ maps considering temporal factors such as seismic ground shaking, seismic amplification at surface level, and rainfall are limited. Hence this study presents a comprehensive use of the multi-criteria decision-making (MCDM) method in landslide risk assessment. In this research, we conducted both geospatial and geotechnical analysis to minimize the danger of landslides. Geospatial analysis is performed using high-resolution satellite data to produce landslide causative factors which were given weightage using the MCDM method. The geotechnical analysis includes a slope stability check, which was done to determine the potential landslide slope. The landslide risk map can provide useful information which helps people to understand the risk of living in an area.

Keywords: landslide hazard zonation, PHA, AHP, GIS

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Authors: Hyeongju Jeon, Yonghyun Kim, Yongil Kim

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Synthetic Aperture Radar (SAR) is an effective tool for damage assessment induced by disasters due to its all-weather and night/day acquisition capability. In this paper, the 2017 Colombia landslide was observed using full-polarimetric ALOS/PALSAR-2 data. Polarimetric decompositions, including the Freeman-Durden decomposition and the Cloude decomposition, are utilized to analyze the scattering mechanisms changes before and after-landslide. These analyses are used to detect the damaged areas induced by the landslide. Experimental results validate the efficiency of the full polarimetric SAR data since the damaged areas can be well discriminated. Thus, we can conclude the proposed method using full polarimetric data has great potential for damage assessment of landslides.

Keywords: Synthetic Aperture Radar (SAR), polarimetric decomposition, damage assessment, landslide

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Authors: Deepak Raj Bhat, Kazushige Hayashi, Yorihiro Tanaka, Shigeru Ogita, Akihiko Wakai

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Slow-moving landslides are one of the major natural disasters in mountainous regions. Therefore, study of the creep displacement behaviour of a landslide and associated geological and geotechnical issues seem important. This study has addressed and evaluated the slow-moving behaviour of landslide using the 2D-FEM based Elasto-viscoplastic constitutive model. To our based knowledge, two new control constitutive parameters were incorporated in the numerical model for the first time to better understand the slow-moving behaviour of a landslide. First, the predicted time histories of horizontal displacement of the landslide are presented and discussed, which may be useful for landslide displacement prediction in the future. Then, the simulation results of deformation pattern and shear strain pattern is presented and discussed. Moreover, the possible failure mechanism along the slip surface of such landslide is discussed based on the simulation results. It is believed that this study will be useful to understand the slow-moving behaviour of landslides, and at the same time, long-term monitoring and management of the landslide disaster will be much easier.

Keywords: numerical simulation, ground water fluctuations, elasto-viscoplastic model, slow-moving behaviour

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Javed Iqbal

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Filling of Xiangjiaba Reservoir Lake in Southwest China triggered and re-activated numerous landslides due to water fluctuation. In order to understand the relationship between reservoirs and slope instability, a typical reservoir landslide (Dasha landslide) at right bank of Jinsha River was selected as a case study for in-depth investigations. The detailed field investigations were carried out in order to identify the landslide with respect to its surroundings and to find out the slip-surface. Boreholes were drilled in order to find out the subsurface lithology and the depth of failure of Dasha landslide. The in-situ geotechnical tests were performed, and the soil samples from exposed slip surface were retrieved for geotechnical laboratory analysis. Finally, stability analysis was done using 3D strength reduction method under different conditions of reservoir water level fluctuations and rainfall conditions. The in-depth investigations show that the Dasha landslide is a bedding rockslide which was once activated in 1986. The topography of Dasha landslide is relatively flat, while the back scarp and local terrain are relatively steep. The landslide area is about 29 × 104 m², and the maximum thickness of the landslide deposits revealed by drilling is about 40 m with the average thickness being about 20 m, and the volume is thus estimated being about 580 × 10⁴ m³. Bedrock in the landslide area is composed of Suining Formation of Jurassic age. The main rock type is silty mudstone with sandstone, and bedding orientation is 300~310° ∠ 7~22°. The factor of safety (FOS) of Dasha landslide obtained by 3D strength reduction cannot meet the minimum safety requirement under the working condition of reservoir level fluctuation as designed, with effect of rainfall and rapid drawdown.

Keywords: Dasha landslide, Xiangjiaba reservoir, strength reduction method, bedding rockslide

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Authors: Neşe Işık, Şenol Altıok, Galip Devrim Eryılmaz, Aydın durukan, Hasan Özgür Daş

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In this study, an active landslide zone affecting the road alignment on the Tortum-Uzundere (Erzurum/Turkey) highway was investigated. Due to the landslide movement, problems have occurred in the existing road pavement, which has caused both safety problems and reduced driving comfort in the operation of the road. In order to model the landslide, drilling, geophysical and inclinometer studies were carried out in the field within the scope of ground investigation. Laboratory tests were carried out on soil and rock samples obtained from the borings. When the drilling and geophysical studies were evaluated together, it was determined that the study area has a complex geological structure. In addition, according to the inclinometer results, the direction and speed of movement of the landslide mass were observed. In order to create an idealized geological profile, all field and laboratory studies were evaluated together and then the sliding surface of the landslide was determined by back analysis method. According to the findings obtained, it was determined that the landslide was massively large, and the movement occurred had a deep sliding surface. As a result of the numerical analyses, it was concluded that the Slope angle reduction is the most economical and environmentally friendly method for the control of the landslide mass.

Keywords: landslide, geotechnical methods, geophysics, monitoring, highway

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Authors: Michael Bellas, George Voulgaridis

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This paper refers to the world known mega - landslide induced at the community of Ropoto, belonging to the Municipality of Trikala, in the Central part of Greece. The landslide affected the debris as well as the colluvium mantle of the flysch, and makes up a special case of study in engineering geology and geotechnical engineering not only because of the size of the domain affected by the landslide (approximately 750m long), but also because of the geostructure’s global behavior. Due to the landslide, the whole community’s infrastructure massively collapsed and human lives were put in danger. After the complete simulation of the coupled Seepage - Deformation phenomenon due to the extreme rainfall, and by closely examining the slope’s global behavior, both the mitigation of the landslide, as well as, an advanced surveillance method (Fiber Bragg Grating) using fiber optics were further studied, in order both to retain the geostructure and to monitor its health by creating an early warning system, which would serve as a complete safety net for saving both the community’s infrastructure as well as the lives of its habitats.

Keywords: landslide, remediation measures, the finite element method (FEM), Fiber Bragg Grating (FBG) sensing method

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Authors: Ji-Yuan Lin, Yu-Ming Liou, Yi-Ting Chen, Chen-Syuan Lin

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Soil water index obtained by tank model is now commonly used in soil and sand disaster alarm system in Japan. Comparing with the rainfall trigging index in Taiwan, the tank model is easy to predict the slope water content on large-scale landslide. Therefore, this study aims to estimate the threshold value of large-scale landslide using the soil water index Sixteen typhoons and heavy rainfall events, were selected to establish the, to relationship between landslide event and soil water index. Finally, the proposed threshold values for landslides on country road No.89 are suggested in this study. The study results show that 95% landslide cases occurred in soil water index more than 125mm, and 30% of the more serious slope failure occurred in the soil water index is greater than 250mm. Beside, this study speculates when soil water index more than 250mm and the difference value between second tank and third tank less than -25mm, it leads to large-scale landslide more probably.

Keywords: soil water index, tank model, landslide, threshold values

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Authors: Mengqi Wu, Haiping Zhu, Chin J. Leo

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In this study, six experimental cases with different components (dry and wet soils and rocks) were considered to elucidate the influence of material composition on landslide profiles. The results show that the accumulation zone for all cases considered has a quadrilateral shape with two different bottom angles. The asymmetry of the accumulation zone can be attributed to the fact that soils in different parts of the landslide sliding can produce different speeds and suffer different resistances. The higher soil moisture can generate stronger cohesion between soils to reduce the volume of the sliding body during the landslide. The rock content can increase the accumulation angles to improve slope stability. The interaction between the irregular shapes of rocks and soils provides more resistance than that between spherical rocks and soils, which causes the slope with irregular rocks and soils to have higher stability.

Keywords: landslide, soil moisture, rock content, experimental simulation

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Authors: ZerarkaHizia, Akchiche Mustapha, Prunier Florent

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Landslide of Ain El Hammam (AEH) has been an old slip since 1969; it was reactivated after an intense rainfall period in 2008 where it presents a complex shape and affects broad areas. The schist of AEH is more or less altered; the alteration is facilitated by the fracturing of the rock in its upper part, the presence of flowing water as well as physical and chemical mechanisms of desegregation in joint of altered schist. The factors following these instabilities are mostly related to the geological formation, the hydro-climatic conditions and the topography of the region. The city of AEH is located on the top of a steep slope at 50 km from the city of TiziOuzou (Algeria). AEH’s topographic monitoring of unstable slope allows analyzing the structure and the different deformation mechanism and the gradual change in the geometry, the direction of change of slip. It also allows us to delimit the area affected by the movement. This work aims to study the behavior of AEH landslide with topographic monitoring and to validate the results with numerical modeling of the slip site, when the hydraulic factors are identified as the most important factors for the reactivation of this landslide. With the help of the numerical code PLAXIS 2D and PlaxFlow, the precipitations and the steady state flow are modeled. To identify the mechanism of deformation and to predict the spread of the AEH landslide numerically, we used the equivalent deviatory strain, and these results were visualized by MATLAB software.

Keywords: equivalent deviatory strain, landslide, numerical modeling, topographic monitoring

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Authors: Akbar Khodavirdizadeh

Abstract:

Gougerd village landslide with area about 150 hectares is located in southwest of Khoy city in northwest of the Iran. This Landslide was commenced more than 21 years and caused some damages in houses like some fissures on walls and some cracks on ground and foundations. The main mechanism of landslide is rotational with the high different of top and foot is about 230 m. The thickness of slide mass based on geoelectrical investigation is about 16m obtained. The upper layer of slope is silty sand and the lower layer of clayey gravel. In this paper, the stability of landslide are analyzed based in static analysis under different groundwater surface conditions and at slope angle changes with limit eqlibrium method and the simplified Bishop method. The results of the 72 stability analysis showed that the slope stability of Gougerd landslide increased with increasing of the groundwater surface depth of slope crown. And especially when decreased of slope angle, the safety facter more than in previous state is increased. The required of safety factor for stability in groundwater surface depth from slope crown equal 14 m and with decreased of slope angle to 3 degree at decrease of groundwater surface depth from slope crown equal 6.5 m obtained. The safety factor in critical conditions under groundwater surface depth from slope crown equal 3.5 m and at decreased of slope angle to 3 degree equal 0.5 m obtained. At groudwater surface depth from slope crown of 3 m, 7 m and 10 m respectively equal to 0.97, 1.19 and 1.33 obtained. At groudwater surface depth from slope crown of 3 m, 7 m and 10 m with decreased of slope angle to 3 degree, respectively equal to 1.27, 1.54 and 1.72 obtained. According to the results of this study, for 1 m of groundwater level decrease, the safety factor increased by 5%, and for 1 degree of reduction of the slope angle, safety factor increased by 15%. And the effect of slope angle on Gougerd landslide stability was felt more than groundwater effect.

Keywords: Gougerd landslide, stability analysis, slope angle, groundwater, Khoy

Procedia PDF Downloads 143

Authors: Antonello Troncone, Luigi Pugliese, Andrea Parise, Enrico Conte

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The present study deals with the landslides periodically reactivated by groundwater level fluctuations owing to rainfall. The main type of movement which generally characterizes these landslides consists in sliding with quite small-displacement rates. Another peculiar characteristic of these landslides is that soil deformations are essentially concentrated within a thin shear band located below the body of the landslide, which, consequently, undergoes an approximately rigid sliding. In this context, a simple method is proposed in the present study to forecast the movements of this type of landslides owing to rainfall. To this purpose, the landslide body is schematized by means of a two-block model. Some analytical solutions are derived to relate rainfall measurements with groundwater level oscillations and these latter, in turn, to landslide mobility. The proposed method is attractive for engineering applications since it requires few parameters as input data, many of which can be obtained from conventional geotechnical tests. To demonstrate the predictive capability of the proposed method, the application to a well-documented landslide periodically reactivated by rainfall is shown.

Keywords: rainfall, water level fluctuations, landslide mobility, two-block model

Procedia PDF Downloads 101

Authors: Fatemeh Nemati, Lucinda Leonard, Gwyn Lintern, Richard Thomson

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In this study, we will use modern numerical modeling approaches to estimate tsunami risks to the southern coast of British Columbia from landslides. Wave generation is to be simulated using the NHWAVE model, which solves the Navier-Stokes equations due to the more complex behavior of flow near the landslide source; far-field wave propagation will be simulated using the simpler model FUNWAVE_TVD with high-order Boussinesq-type wave equations, with a focus on the accurate simulation of wave propagation and regional- or coastal-scale inundation predictions.

Keywords: FUNWAVE-TVD, landslide-generated tsunami, NHWAVE, tsunami risk

Procedia PDF Downloads 123

Authors: Deepak Raj Bhat, Akihiko Wakai, Shigeru Ogita, Yorihiro Tanaka, Kazushige Hayashi, Shinro Abe

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In this study, the newly developed finite element methods are used for numerical analysis ofa landslide triggered by the fluctuations of ground-water levels in different cases I-IV. In case I, the ground-water level is fixed in such a way that the overall factor of safety (Fs) would be greater or equal to 1 (i.e., stable condition). Then, the ground-water level is gradually increased up to 1.0 m for, making the overall factor of safety (Fs) less than one (i.e., stable or moving condition). Then, the newly developed finite element model is applied for numerical simulation of the slope for each case. Based on the numerical analysis results of each Cases I-IV, the details of the deformation pattern and shear strain pattern are compared to each other. Moreover, the change in mobilized shear strength and local factor of safety along the slip surface of the landslide for each case are discussed to understand the triggering behaviors of a landslide due to the increased in ground water level. It is expected that this study will help to better understand the role of groundwater fluctuation for triggering of a landslide or slope failure disasters, and it would be also helpful for the judgment of the countermeasure works for the prevention and mitigation of landslide and slope failure disasters in near future.

Keywords: finite element method, ground water fluctuations, constitutive model, landslides, long-term disaster management system

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Authors: Can Huang, Xiaoliang Wang, Qingquan Liu

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Soil‒water coupling is an important process in landslide-generated impulse waves (LGIW) problems, accompanied by large deformation of soil, strong interface coupling and three-dimensional effect. A meshless particle method, smooth particle hydrodynamics (SPH) has great advantages in dealing with complex interface and multiphase coupling problems. This study presents an improved soil‒water coupled model to simulate LGIW problems based on an open source code DualSPHysics (v4.0). Aiming to solve the low efficiency problem in modeling real large-scale LGIW problems, graphics processing unit (GPU) acceleration technology is implemented into this code. An experimental example, subaerial landslide-generated water waves, is simulated to demonstrate the accuracy of this model. Then, the Huangtian LGIW, a real large-scale LGIW problem is modeled to reproduce the entire disaster chain, including landslide dynamics, fluid‒solid interaction, and surge wave generation. The convergence analysis shows that a particle distance of 5.0 m can provide a converged landslide deposit and surge wave for this example. Numerical simulation results are in good agreement with the limited field survey data. The application example of the Huangtian LGIW provides a typical reference for large-scale LGIW assessments, which can provide reliable information on landslide dynamics, interface coupling behavior, and surge wave characteristics.

Keywords: soil‒water coupling, landslide-generated impulse wave, large-scale, SPH

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Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

Procedia PDF Downloads 361

Authors: Hasmithaa Neha, Atul Kumar Patidar, Girish Ch Kothyari

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The northward convergence of the Indian plate has a dominating influence over the structural and geomorphic development of the Himalayan region. The highly deformed and complex stratigraphy in the area arises from a confluence of exogenic and endogenetic geological processes. This region frequently experiences natural hazards such as debris flows, flash floods, avalanches, landslides, and earthquakes due to its harsh and steep topography and fragile rock formations. Therefore, remote sensing technique-based examination and real-time monitoring of tectonically sensitive regions may provide crucial early warnings and invaluable data for effective hazard mitigation strategies. In order to identify unusual changes in the river gradients, the current study demonstrates a spatial quantitative geomorphic analysis of the upper Alaknanda River basin, Uttarakhand Himalaya, India, using gradient length anomaly analysis (GLAA). This basin is highly vulnerable to ground creeping and landslides due to the presence of active faults/thrusts, toe-cutting of slopes for road widening, development of heavy engineering projects on the highly sheared bedrock, and periodic earthquakes. The intersecting joint sets developed in the bedrocks have formed wedges that have facilitated the recurrence of several landslides. The main objective of current research is to identify abnormal gradient lengths, indicating potential landslide-prone zones. High-resolution digital elevation data and geospatial techniques are used to perform this analysis. The results of GLAA are corroborated with the historical landslide events and ultimately used for the generation of landslide susceptibility maps of the current study area. The preliminary results indicate that approximately 3.97% of the basin is stable, while about 8.54% is classified as moderately stable and suitable for human habitation. However, roughly 19.89% fall within the zone of moderate vulnerability, 38.06% are classified as vulnerable, and 29% fall within the highly vulnerable zones, posing risks for geohazards, including landslides, glacial avalanches, and earthquakes. This research provides valuable insights into the spatial distribution of landslide-prone areas. It offers a basis for implementing proactive measures for landslide risk reduction, including land-use planning, early warning systems, and infrastructure development techniques.

Keywords: landslide vulnerability, geohazard, GLA, upper Alaknanda Basin, Uttarakhand Himalaya

Procedia PDF Downloads 40

Authors: Cindy Sahera, Sarwono Sutikno

Abstract:

Rapid technological developments also have negative impacts, namely the increasing criminal cases based on technology or cybercrime. One technique that can be used to conduct cybercrime attacks are phishing email. The issue is whether the user is aware that email can be misused by others so that it can harm the user's own? This research was conducted to measure the susceptibility of selected targets against email abuse. The objectives of this research are measurement of targets’ susceptibility and find vulnerability in email recipient. There are three steps being taken in this research, (1) the information gathering phase, (2) the design phase, and (3) the execution phase. The first step includes the collection of the information necessary to carry out an attack on a target. The next step is to make the design of an attack against a target. The last step is to send phishing emails to the target. The levels of susceptibility are three: level 1, level 2 and level 3. Level 1 indicates a low level of targets’ susceptibility, level 2 indicates the intermediate level of targets’ susceptibility, and level 3 indicates a high level of targets’ susceptibility. The results showed that users who are on level 1 and level 2 more that level 3, which means the user is not too careless. However, it does not mean the user to be safe. There are still vulnerabilities that may occur, such as automatic location detection when opening emails and automatic downloaded malware as user clicks a link in the email.

Keywords: cybercrime, email phishing, susceptibility, vulnerability

Procedia PDF Downloads 254