Landslide Factors Research Articles

Inventory Of Landslides Triggered By Hurricane Matthews In Guantánamo, Cuba

Hurricane Matthew affected the eastern region of Cuba from October 4th to 5th causing large damages and numerous landslides. This research presents an inventory of landslides triggered by the hurricane. Visual interpretation of satellite images of moderate resolution from Sentinel 2A instrument and localized higher resolution satellite images provided by PlanetScope, as well as field research were the main sources of information. The resulting landslide inventory was compared with other landslide factors such as slope, geology, and soil deep and composition from maps at a scale of 1:100 000. Data recorded by 1-hour rain gauges and 24-hour rain gauge was also analyzed in order to identify rainfall thresholds for the occurrence of landslides during the Hurricane Matthew influence in the study region. A total of 619 landslides were identified and classified as rockslide, rockfall or debris flows. The research found the slope was not as important factor as the type of rock. Most of landslides were located in areas of green shale of volcanic and vulcanoclastic rocks and rocks of the ophiolitic complex formed by ancient remnants of oceanic crust. The accumulate rainfall threshold estimated for the event was between 178-407 mm/day.

Joint Effects and Spatiotemporal Characteristics of the Driving Factors of Landslides in Earthquake Areas

Joint Effects and Spatiotemporal Characteristics of the Driving Factors of Landslides in Earthquake Areas

Probability analysis of rock slope using FORM based on a nonlinear strength criterion

Various uncertainties are frequently encountered in the assessment of landslide stability. However, the uncertainty of parameters in the Mohr–Coulomb strength criterion is a major concern in the studies, neglecting the uncertainty related to the strength criterion itself that is used to describe the strength of geotechnical materials. To assess the reliability of landslides, a new probabilistic analysis method is proposed in this study. According to the traditional limit equilibrium framework, an improved method based on a nonlinear strength criterion is deduced to determine the safety factor of landslides. The local mean value of the random variables on the failure surface is estimated by the analytical method, avoiding random field simulation. The first-order reliability method (FORM) is applied to estimate the failure probability of landslides. An example is analyzed using the proposed probability analysis method, and its effectiveness is primarily verified. The results show that the traditional limit equilibrium method based on the linear Mohr–Coulomb strength criterion estimates the stability and failure probability of landslides inaccurately.

Fuzzy Logic Regional Landslide Susceptibility Multi-Field Information Map Representation Analysis Method Constrained by Spatial Characteristics of Mining Factors in Mining Areas

Landslide susceptibility analysis has become a necessary means of pre-disaster portal positioning and scientific early warning. How can an effective zoning model of landslide susceptibility be established to examine the important factors affecting landslide development in coal mine areas? Focusing on the need for a reliability analysis of landslide susceptibility in coal mine areas, landslide cataloging and environmental factor data were used as objects, combined with the knowledge of landslide mechanisms, disaster environmental factors and the spatial correlation of landslide disasters, the frequent landslide area of Jiumine in the main part of Xishan Coalfield was selected as the research area, and more than 50 influencing factors were collected and calculated. Eighteen factors with correlation coefficients of less than 0.3 were selected, and a landslide susceptibility analysis method combining the spatial characteristics of landslide factors and the heuristic fuzzy logic model was proposed. The influence of the fuzzy logic model on the accuracy of landslide susceptibility analysis results under different constraint modes was tested. The model is a mixture of knowledge-driven and data-driven models, and is compared with information model and SVM. Experimental results show that the proposed method is feasible and reliable, and improves the accuracy of model results.

Landslide Susceptibility Assessment Using the Analytic Hierarchy Process (AHP): A Case Study of a Construction Site for Photovoltaic Power Generation in Yunxian County, Southwest China

China is actively promoting the construction of clean energy to reach its objective of achieving carbon neutrality. However, engineering constructions in mountainous regions are susceptible to landslide disasters. Therefore, the assessment of landslide disaster susceptibility is indispensable for disaster prevention and risk management in construction projects. In this context, the present study involved conducting a field survey at 42 landslide points in the selected planned site region. According to the geological and geographical conditions of the study region, the existing regulation, and the influencing factors of landslides, the assessment in the field survey was performed based on 11 impact factors, namely, the slope, slope aspect, curvature, relative relief, NDVI, road, river, fault, lithology, the density of the landslide points, and the land-use type. Next, based on their respective influences, these impact factors were further divided into subfactors according to AHP, and the weights of each factor and subfactor were calculated. The GIS tools were employed for linear combination calculation and interval division, and accordingly, a landslide susceptibility zone map was constructed. The ROC curve was adopted to test the partition evaluation results, and the AUC value was determined to be 0.845, which indicated the high accuracy of the partition evaluation results.

Analysis of the effects of vertical joints on the stability of loess slope

Vertical joints with different lengths and slope distances (the horizontal distance from joints to slope shoulder) are generated at the top of the loess slope because of the unloading and collapsibility of the loess. The existence of vertical joints is a significant risk factor for loess landslides. This paper applies three methods, including laboratory tests, numerical simulation and theoretical analysis, to analyze the influence of vertical joints on the stability of loess slope. Firstly, the typical failure mode and strength characteristics of the samples containing vertical joints are analyzed through the unconfined compressive strength test and triaxial compression test. Further, the effects of vertical joints with different lengths and the slope distance on the slope safety coefficient and sliding surface position are calculated by finite element numerical analysis. Finally, the formula for calculating the safety coefficient of loess slope with vertical joints is derived. The results show that: the joint bonding degree affects the sample's strength and failure rate. The strength of the sample with open vertical joints is less than that of closed ones, and the failure rate is greater than that of closed ones. When the length and the slope distances of the vertical joint are less than 5 m, the influence of vertical joints on the shape and position of the sliding surface will be small. When the length of the vertical joint is more than 5 m and less than 15 m, the safety factor of the slope decreases with the increase in the length of vertical joints. When the length of vertical joints reaches 15 m, and the slope distance is 10 m, the deterioration degree of vertical joints on the slope comes up to the peak. Statistical analysis shows that the dominant dip angle of shear fracture at the back edge of the landslide affected by the vertical joint surface is between 45° and 65°. The research results can be used for rapid calculation of the safety coefficient and rapid evaluation of slope stability of loess slope with vertical joints.

Geospatial approach for Landslide Susceptibility Mapping techniques: A Case Study of Ratnagiri District, Maharashtra, India

Natural and anthropogenic pressure leads to landslides which are catastrophic disaster. Due to climate and land use changes, increase in population landslides has been increasing, specifically, in the mountainous areas. It is threat to lives and their properties. It is necessary to understand geophysical setup of any region causing landslides. It is better to have scientific approach to identify landslide potential risk zones. There are several advanced techniques to study landslides. Here analytical hierarchical process and frequency ratio method incorporated with geospatial technology are utilized for identification of conditioning factors for landslides and landslide susceptibility mapping. Most of the landslide susceptible area are located in the eastern and some middle part of study region (Ratnagiri District). The landslide possibilities are very high along the riverside, high slope region and lineament surrounding. Very high susceptible zone is found to be located mainly in middle part of Chiplun, Devrukh, Rajapur and Dapoli, the southern part of Mandangarh, Khed and eastern part of Ratnagiri. The eastern part of Khed, Chiplun, Devrukh and Lanja falls under the high susceptible zone. Middle part of Guhagar, northern part of Ratnagiri and Rajapur fall under moderate susceptible zone. The southern part of Ratnagiri and western side of Rajapur fall under low susceptible zone. Proper planning and development in terms of disaster mitigation management are needed. No further construction near landslide hazards should be allowed. New laws/regulations should also restrict further construction around the zone of slope failures.

Influencing Physical Characteristics of Landslides in Kuala Lumpur, Malaysia

Landslide is one of the natural disasters that commonly occurs in terrestrial environments with slopes throughout the world. Located among countries with tropical climates, the hot and humid conditions expose Kuala Lumpur, Malaysia, to the risk of landslides. This paper aims to delineate the influencing physical characteristics of landslide occurrences in Kuala Lumpur. In this study, a 100 landslides historical data set and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, such as distance to roads, distance to streams, elevation, slope angle, curvature, slope aspect, land use, and lithology. These factors were processed using GIS as geospatial analysis provides a useful tool for planning, disaster management, and hazard mitigation. By using ArcMap 10.8.2, a GIS software, different spatial analyses in which maps for each physical factor were layered with landslide events distribution. The weights for each factor were determined using the ANN approach resulting in the slope angle having the highest relative importance with a 100.0% value. In comparison, 8.3% represents the slope aspect as the most insignificant factor out of the eight selected characteristics for this study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition have to be established for future mitigation action to support the agenda of SDG 15.

Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

<span lang="EN-US">In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.</span>

Assessing landslide susceptibility using geospatial technology in Bonga town, southwestern Ethiopia

ABSTRACT This study aims to investigate the main factors that contribute to frequent landslides in Bonga town using GIS and remote sensing. To this end, a frequency ratio model in integration with geospatial technology was applied. The first input parameter (landslide inventory data) was collected and randomly categorized into training and validation. The second input parameters were causative landslide factors such as slope, aspect, curvature, elevation, land use, lithology, stream distance, and distance to the road. The contributing rates of landslide causative factors were calculated using the frequency ratio model based on causative factors and landslide training data. The study found that distance to the road, distance to the stream, elevation, and aspect were the most important factors for the occurrence of landslides in Bonga town. Land use and slope were the moderate contributing factors to landslide occurrence, while curvature and lithology were the least important contributing factors. The landslide susceptibility index map shows that the largest portion of the study area falls under the moderate (42.3%,) and high (34.3%) susceptibility zones. Hence, different intervention strategies for saving lives and assets should be developed swiftly based on the vulnerability level of each site.

Landslide Vulnerability of Urban slums: Case of Warje Slum, Pune, Maharashtra

The rapid urbanization in developing countries contributed to the severity of urban environmental hazards such as slope failure and flooding. In addition, heavy rainfall or alterations to natural environmental characteristics trigger the incidence of hazards such as landslides where local topographic conditions often exacerbate the vulnerability of the built environment. Landslide causes numerous fatalities and financial damage to millions worldwide and India is no exception. In most of the Indian cities, the physical expansion during the past few decades has resulted in increased vulnerability, with the occupation of hill slopes subject to instability. Therefore, the impact of physical characteristics of the environment and human interventions needs to be examined in assessing urban vulnerability. This study examines the vulnerability of urban settlements to landslide risk with Warje Slum, Pune, Maharashtra as a case study. It attempts to understand better the causative factors of landslides and their impact to suggest ways for better disaster management to save life and property in the future.

LANDSLIDE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS STUDY CASE AL HOCEIMA REGION, NORTHERN MOROCCO

Abstract. Landslides are one of the most dangerous natural disasters worldwide. Al Hoceima region, Part of the Moroccan mountain chain of the Rif is not an exception, since it’s dominated by relatively young reliefs and marked by its dynamics compared to other regions. The main goal of this study is to assess the performance of Machine learning algorithms and identify the optimal method for the mapping of the area susceptible to landslides, in Al Hoceima city and its periphery, The current study aimed at evaluating the capabilities of six advanced machine learning algorithms including, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), XGBoost (XGB) and Logistic Regression (LR). A total of 114 landslides were mapped from various sources. 70% of this database was used for model building and 30% for validation. Ten landslide factors are selected to detect the most sensitive areas: altitude, slope, aspect, distance to faults, distance from roads, lithology, curvature, plan curvature, profile curvature, and vegetation index (NDVI). The outcome of the landslide susceptibility analysis was verified using receiver operating characteristics (ROC) curves and precision-recall curves (PRC), acknowledging XGBoost and Random Forest as the optimal methods with AUCROC (96% and 95.5% accordingly), a severe imbalanced classification was detected by the PRC, solved by undersampling the majority class, to obtain major improvement in models performance according to AUCPRC (from 40% to 87% for XGBoost) and slight decrease according to AUCROC (from 96% to 94% for XGBoost). The outcome of this study and the landslide susceptibility maps would be useful for environmental, economic, and social protection and to help formulate suggestions for optimizing landslide risk assessment in areas exposed to this phenomenon.

Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas.

Characterization of sliding surface deformation and stability evaluation of landslides with fiber–optic strain sensing nerves

Deformation monitoring of sliding zones or surfaces is of great significance to investigate evolution stages and triggering factors of landslides. The fiber–optic strain sensing cables, similar to human nerves, are capable of capturing slight movements in a distributed sensing and nearly real–time manner. However, the interaction between fiber–optic strain sensing nerves and surrounding geomaterials under shearing remains unclear. In this study, a novel analytical methodology for interpreting the cable–soil interaction under shear deformation and a reliable solution to calculate the shear displacements of critical interfaces in landslides are presented. The feasibility of the proposed method is proven through laboratory shear tests and later applied to calculate the shear displacements of the Majiagou landslide located in the Three Gorges Reservoir region, China. Finally, the empirical relationships between the measured strains and factors of safety are established. It is proved that the reservoir water level drawdown and rainfall cause accelerated shear deformation of this landslide.

Key predisposing factors and susceptibility assessment of landslides along the Yunnan–Tibet traffic corridor, Tibetan plateau: Comparison with the LR, RF, NB, and MLP techniques

The Yunnan–Tibet traffic corridor runs through the Three Rivers Region, southeastern Tibetan Plateau, which is characterized by high-relief topography and active tectonics, with favourable conditions for landslides. It is of great significance to identify the key predisposing factors of landslides and to reveal the landslide susceptibility in this area. A total of 2,308 landslides were identified as learning samples through remote sensing interpretation and detailed field surveys, and four machine learning algorithms involving logistic regression (LR), random forest (RF), naïve Bayes (NB) and multilayer perceptron (MLP) were compared to model the landslide susceptibility. Through the multicollinearity test, 13 influential factors were selected as conditioning factors. The area under the curve (AUC) values of LR, RF, NB and MLP models are .788, .918, .785 and .836 respectively, indicating that the four models have good or very good prediction accuracy in landslide susceptibility assessment along the Yunnan–Tibet traffic corridor. In addition, the elevation, slope, rainfall, distance to rivers, and aspect play a major role in landslide development in the study area. The susceptibility zoning map based on the best RF model shows that the areas with high susceptibility and very high susceptibility account for 12.24% and 6.72%, respectively, and are mainly distributed along the Jinsha River, the Lancang River and the G214 highway.

Spatial Analysis on the Variances of Landslide Factors Using Geographically Weighted Logistic Regression in Penang Island, Malaysia

Landslides are one of the common natural disasters involving mostly movement of soil surfaces associated with gravitational attraction. Their adverse losses and significant damage, which always result in at least 17% of casualties and billions of dollars of financial losses worldwide, have made landslides the third most notorious phenomenon devastating many parts of the world. Malaysia has had multiple landslide occurrences, particularly in highly urbanized areas, such as Penang Island, owing to the declining vegetation cover in hilly terrains. Thus, this study aims to delineate the spatial relationship variances between landslide occurrences and the influencing factors in the area of interest. Ten influencing factors considered, including distance to roads, distance to rivers, distance to faults, slope angle, slope aspect, curvature, rainfall annual average, lithology, soil series, and land use. In this study, we use a software (GWR 4.0) as a medium for the analysis processing, coupled with GIS. A local statistical technique, Geographically Weighted Logistic Regression (GWLR), is primacy in capturing the geographical variation of the model coefficients that considers non-stationary variables and models their relationships, as well as processes regression coefficients over space. Goodness-of-fit criteria were used to evaluate the GWLR model, namely AICc that decrease from 872.202167 to 800.856998. Bayesian Information Criterion (BIC) shows a decrease in value from 925.784185 to 945.196942. Likewise, deviance decreased from 849.931675 to 739.175630, while pdev increased from 0.379457 to 0.460321. These goodness-of-fit criteria values express GWLR as the best model for local measure. The variances in both local parameter estimates and the t-values (negative and positive values) show the level of significance for each landslide factor in influencing landslide occurrences across the study area. The results of the local parameter estimates and the t-values also show that the slope angle and the slope aspect spatially affect landslide occurrences across the study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition must be established for future mitigation actions to support the agenda of SDG 15, which promotes resilience and disaster risk reduction.

Elevation Angles, Soil Textures, Soil Settlements and WaterHolding Capacity on Landslides: An Experimental Case Study in the Province of Iloilo, Philippines

The soil textures, soil settlements, soil water-holding capacity, and elevation angles are parameters for landslides in the Province of Iloilo. They are usually triggered during heavy rainstorms, causing severe property damage and sometimes causing casualties. This experimental study aims to determine how these parameters are factors for landslides and flood monitoring and uses three methods to provide ease and continuity of measurements. The Fourier transforms infrared (FTIR)spectroscopy analysis for soil textures, the Imhoff cone for the settling and water-holding capacity of the soil, and an improvised apparatus to test how the slopes affect the transportation of soil materials. FTIR Soil analysis reveals that contents of clay and organic matter directly affect soil water-holding capacity due to the larger surface area. The smaller particle sizes, such as clay, have a larger surface area leading to a higher water-holding ability. A landslide-prone zone has a lesser settling time except for the sand that settles fastest due to larger masses. An angle of 20 degrees has more mass movement too much higher slopes like 55 and 85 degrees. The increase in height or angle of the elevation decreases stability, leading to less mass movement. Less mass movement leads to the conclusion that there are rare landslides in the very high slope range due to fewer vibrations or soil movements caused by human activities and being compact or stable. This study is crucial for forecasting and preventing geohydrological processes and developing better landslide warning strategies to mitigate risks and reduce socioeconomic damage.

Use of Drone Technology in Georeferencing Slopes of Landslide Prone Areas

Access to regions that cannot be accessed owing to natural damage and contours that are too extreme present challenges in mapping areas prone to landslides, requiring the use of drones to retrieve data from disaster areas. The goal of this research is to (1) use arcgis to reference landslide-prone locations, (2) analyze the safe level value of landslide-prone areas using slide applications, and (3) use drone photogrammetry to model landslide-prone areas in 3D. At locations 119°40′3.36"E - 119°40′30.58"E and 5°19′32.53"S - 5°20′3.69"S, photogrammetry was employed by taking ortho photographs with mavic 2 pro type drones and secondary data of the soil layer provided by penetration test. The area of S01 with FoS 0.470 on the slope of slope 51°, the area S02 with FoS 0,527 on the slope of slope 47°, the area S03 with FoS 0.379 on the slope of slope 52°, the area S04 with FoS 0.551 on slope 46°, and the area S05 with FoS 0.763 on slope 32° are all known from the referencing map of arcgis-based landslide prone areas. The concluded that of soil in the research area is generally sandy clay soil, and the position of rainwater flow, which can be a trigger factor for landslides, so this data becomes preliminary data in early anticipation of potential landslide disasters and enhances alertness to prospective landslide disasters.

Spatial analysis and controlling factors of landslides in East Icelandic fjords

The fjords of eastern Iceland display many landslide landforms. These phenomena, visible by remote observation of satellite images and Digital Elevation Models (DEM), were inventoried then measured and analysed. A total of 290 landslides were recorded in a spreadsheet database and in a Geographic Information System (GIS). For each landslide, location, morphometry (length, width, surface area, thickness, estimated volume, etc.) data, as well as potential control variables, particularly geological (lithology, dip) or explanatory (orientation, age of deglaciation of the slope affected by the landslide) were recorded. These variables and their distribution were studied by spatial and statistical analysis. This study highlights a higher density of landslides in the northern part of the study area, which could be explained by past ice sheet current directions, which would have reinforced the pressure exerted on the slopes leading to the postglacial decompression phenomenon. The over-representation of west- and south-facing landslides leads to the hypothesis of climatic control: the sunnier slopes underwent more rapid deglaciation, which favoured the instability of the slopes. The data collected also suggest that the lithology (Tertiary basalts) is a control variable and the period during which the landslide area is deglaciated an explanatory variable for landslide initiation. The landslides observed are thus part of a paraglacial dynamic of slope instability after their deglaciation.

Accuracy Improvement of Different Landslide Susceptibility Evaluation Models through K‐Means Clustering: A Case Study on China’s Funing County

Landslides occur in most countries. As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide‐prone areas for various construction projects. In recent years, supervised machine learning algorithms have been widely used in landslide susceptibility evaluation, but many flaws remain in the selection of nonlandslide point samples for comparative analysis. It is significant to improve the authenticity of sample data and reduce the impact of noise. China’s Funing County was used as a case study in this paper, which first identified 122 landslide incidents based on historical data, fieldwork, and remote sensing images to create a landslide inventory in the research area. In addition, 12 causal factors of landslides were determined, including elevation, slope, aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, normalized difference vegetation index (NDVI), lithology, and land cover. K‐means clustering was used to purify the factor data factors before data‐driven certainty factor (CF) and frequency ratio (FR) models, and machine learning models, random forest (RF) and artificial neural network (ANN), were used for a comparative study on landslide susceptibility evaluation in Funing County. The results show that the selection method of nonlandslide sample data will affect the accuracy of different evaluation models. The purified sample data improved the prediction accuracy of the four models, with significant prediction accuracy improvements observed in the ANN model. The purification of nonlandslide sample data by K‐means method is of great significance for the drawing of landslide sensitivity map.