Landslide Triggering Factors Research Articles

Application of novel framework approach for assessing rainfall induced future landslide hazard to world heritage sites in Indo-Nepal-Bhutan Himalayan region

The present study focused on mapping of ‘landslide susceptibility (LS)’ and evaluating landslide risk on some important ‘world heritage sites (WHSs)’ in Indo-Nepal-Bhutan Himalayan. LS mapping was carried out using ‘eXtreme gradient boosting (XGBoost)’, ‘Random Forest (RF)’, and ‘convolutional neural network (CNN)’, considering 15 conditioning factors, including seismicity and rainfall. Since rainfall is the triggering factor of landslides, the future rainfall was estimated using four ‘Shared socioeconomic pathways (SSPs)’ scenarios of the ‘Climate Model Intercomparison Project-6 (CMIP-6)’ to identify the future LS and vulnerable WHSs of the Himalayan. The XGBoost is the robust model applied in future scenario-based LS assessments. The very high susceptibility zone has an increased tendency, about 13% to 31% area in the future scenarios where the predicted rainfall also increased, about 100 mm in 80 years. The findings of this study will aid strategy makers in conserving the heritage monument while also ensuring sustainability.

Cyclic Behaviour of Uniform Sand in Drained and Undrained Conditions at Low Confining Stress in Small-Scale Landslide Model

Earthquakes are one of the main landslide-triggering factors. Earthquakes induce cyclic shear stresses and strains in soil and raise pore water pressure, degrading soil strength towards slope failure. Slopes on which shallow landslides can form due to geological conditions are most susceptible to such failures, mainly because of low confining stress. Effective confining stress has a significant role on soil response during static and cyclic loading. Under low confining stress, soil exhibits dilatant behaviour and can degrade rapidly under cyclic loading. This paper presents the results of undrained and drained cyclic triaxial tests on uniform sandy soil material used for modelling small-scale shallow landslides under 1g conditions. Based on the scaling laws for a model in 1g conditions, the frequencies of straining amplitudes for strain-controlled cyclic triaxial tests were defined. Undrained and drained cyclic triaxial tests on the type of sand used in small-scale landslide models were performed. The sandy samples had been previously consolidated at low confining stress corresponding to the values present in the shallow landslides used as the prototype. The influences of loading frequency and low confining stress on sand behaviour were investigated, and simple models for pore pressure build-up and volumetric strain on sand behaviour were proposed. These simple models can be used to estimate increasing pore water pressure and volumetric strain due to cyclic loading at low confining stress for frequencies lower than 1 Hz. The proposed models show good correlation with the data obtained in the laboratory test.

INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN

Abstract. Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction.

A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia

Despite great advances in remote sensing technologies, accurate satellite information is sometimes challenged in tropical regions where dense vegetation prevents the instruments from retrieving reliable readings. In this work, we introduce a satellite-based landslide rainfall threshold for the country of Colombia by studying 4 years of rainfall measurements from The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for 346 rainfall-triggered landslide events (the dataset). We isolate the two successive rainy/dry periods leading to each landslide to create variables that simulate the dynamics of antecedent wetness and dryness. We test the performance of the derived variables (Rainfall Period 1 (PR1), Rainfall Sum 1 (RS1), Rainfall Period 2 (PR2), Rainfall Sum 2 (RS2), and Dry Period (DT)) in a logistic regression that includes three (3) static parameters (Soil Type (ST), Landcover (LC), and Slope angle). Results from the logistic model describe the influence of each variable in landslide occurrence with an accuracy of 73%. Subsequently, we use these dynamic variables to model a landslide threshold that, in the absence of satellite antecedent soil moisture data, helps describe the interactions between the dynamic variables and the slope angle. We name it the Landslide Triggering Factor—LTF. Subsequently, with a training dataset (65%) and one for testing (35%) we evaluate the LTF threshold performance and compare it to the well-known event duration (E-D) threshold. Results demonstrate that The LTF performs better than the E-D threshold for the training and testing datasets at 71% and 81% respectively.

Landslide deformation and temporal prediction of slope failure in Himalayan terrain using PSInSAR and Sentinel-1 data

Landslides in hilly terrains are frequent phenomenon due to various triggering factors such as heavy rainfall, flash flood, slope instability, tectonic disturbances etc., and result in loss of human lives and damage of infrastructure and property. The continuous monitoring of slope instability and temporal prediction of slope failure is of paramount importance for assessing landslide-triggering factors and mitigating landslide hazards. This research presented a comprehensive methodology for time-series displacement measurement of a landslide-affected area prior to its actual failure and temporal prediction of slope failure in a hilly terrain using persistent scatterer SAR interferometry (PSInSAR) technique and Sentinel-1 data in an open source computational environment. On 30 July 2021, a massive landslide witnessed in Sirmaur district of Himachal Pradesh, India near Barwas and brought down around 180 m stretch of national highway (NH) 707 connecting Paonta Sahib with Shillai-Hatkoti. We used 52 single look complex (SLC) images in ascending and 41 SLC images in descending orbit with VV polarization, acquired for a period of almost two years (2019–2021) for time-series displacement measurement along radar line of sight (LOS), horizontal and vertical directions. The pre-failure displacement-time series derived from ascending and descending data stacks was critically analyzed to identify the reliable and accelerating persistent scatterer (PS) locations for predicting temporal failure of slope material and to understand kinematics of the study area. We used inverse velocity (INV) and modified inverse velocity (T-INV) methods at three different accelerating and reliable PS locations for temporal prediction of slope failure. The results show that the mean LOS velocity ranges from −25.49 to + 31.35 mm/y in ascending data and from −20.97 to + 24.16 mm/y in descending data, and horizontal and vertical components of motion vector vary from −30.23 to + 18.99 mm/y and –23.16 to 25.79 mm/y respectively in the study area. Displacement time-series at selected measurement points (PSs) depict the initiation of slope instability from 31 July 2020 and 3 July 2020 using ascending and descending stacks respectively, which are almost one year before the actual failure. INV method predicted temporal windows of slope failure between 27 July 2021 to 1 August 2021 for ascending stack and 24 July 2021 to 29 July 2021 using descending stack, while those of T-INV method lye between 28 July 2021 to 1 August 2021 for ascending data and between 22 July 2021 to 30 July 2021 for descending data. The deformation-time series and predicted days of slope failure are in good agreement at different reliable PS locations. Results in this study show that the approach used for time failure may evolve as an effective tool for landslide early warning systems.

An Overview: Relationship of Geological Condition and Rainfall with Landslide Events at East Malaysia

Tropical country like Malaysia is rich with residual soil and nurtured with high rainfall amount on average 2,550 mm per year. From 2009 until 2018, there are many landslide events reported in the news at Ranau, Sabah and Canada Hill Miri, Sarawak that occurred during rainy season and the landslide recurs within same location over the years. The objective of this study is to determine the relationship of landslide events with geological condition and rainfall at Ranau, Sabah and Canada Hill Miri, Sarawak. Historical landslide data were obtained from local news, previous researchers, and local authorities. Integrated review was conducted to meet the objective. In summary, both areas are prone to landslide due to the high average amount rainfall recorded and the geological properties that are susceptible to landslide occurrence namely shale interbedded with sandstone. Sandstone and shale contact are easily accessible by water weaken the contact surface lead to landslides incidents. Besides, Shale classified as highly plastic soil due to high amount of clay. Clay soil depends on its matric suction to sustain its strength towards sliding. Thus, increasing of pore pressure from rain infiltration reduce the matric suction and eventually reduce the shear strength. Ranau is located at seismically active area compare to Miri and other locations in Sabah crossing Lobou-Lobou fault, Mensaban fault and Mesilou fault. Theoretically, slope instability due to earthquake happened because the cementation of soil may be broken and lead to lesser roughness between soil surfaces resulting in reduction of internal friction angle and cohesion of soil.
 HIGHLIGHTS
 
 Rainfall-induced landslides are the common landslides phenomenon worldwide including East Malaysia.
 However, due to the high number of seismic activity, Earthquake-induced landslides should not be neglected as one of slope failure phenomena in East Malaysia
 Combinations of both Rainfall and Earthquake are required to be investigated due to the presence of both landslide triggering factors from historical data at East Malaysia
 
 GRAPHICAL ABSTRACT

Characterizing the Groundwater Flow Regime in a Landslide Recharge Area Using Stable Isotopes: A Case Study of the Urbas Landslide Area in NW Slovenia

Slope stability strongly depends on the prevailing hydrological and hydrogeological conditions. The amount and intensity of precipitation and changing groundwater levels are important landslide triggering factors. Environmental tracers, including the chemical and stable isotope compositions of precipitation and groundwater, were used to gain insight into the groundwater dynamics of the Urbas landslide. The landslide is situated in a mountainous area with steep slopes and high precipitation amount and poses a high risk for the safety of the Koroška Bela settlement that lies downstream. The stable isotope analyses of oxygen-18 (18O) and deuterium (2H) in the precipitation and groundwater were used to estimate the groundwater mean residence time and the average altitude of the landslide recharge area. This information will help to plan and prioritize remedial landslide measures aiming to reduce the recharge of the landslide body and, thus, lower the risk of transformation of the sliding material into debris flow. The results of the chemical analysis of samples taken from springs and a piezometer show a Ca–HCO3 water type. This indicates low water–rock interaction in a landslide area composed of Upper Carboniferous and Permian clastic rocks and points to upper laying carbonate rocks and scree deposits as the main recharge area. Water samples for stable isotope analyses of δ18O and δ2H were collected from a rain gauge, springs, and a piezometer over a two-year period (2018–2020). The estimated mean recharge altitude of the groundwater at sampling points was from approximately 1700 to 1800 m a.s.l. with a mean residence time of 2–5 months.

Multisource and Multilevel Investigations on a Historical Landslide: The 1907 Servigliano Earth Flow in Montemurro (Basilicata, Southern Italy)

Italy is one of the European countries most affected by landslides. In order to mitigate the risk, the analysis of such phenomena should involve a broad spectrum of studies to understand the geological and geomorphological properties of the unstable areas, the geometric features of the landslides and the causes of their trigger, the evolution over time, and the works of risk mitigation taken as well as their effectiveness over time. This article is concerned with multidisciplinary investigations on a historical earth flow occurred in Montemurro (Basilicata, Southern Italy) in 1907. We analyse unpublished archive sources strictly coupled with new geological and geomorphological surveys. Furthermore, to gain information on the geometrical features of the landslide body, geophysical prospections (ERT) is used alongside the field surveys. Lastly, to gain insight on the landslide triggering factors, we employed historical–climatological analysis: in particular, we made use of the monthly simple daily intensity index (SDII) to evaluate extreme events and the standardised precipitation index (SPI) to consider previous wetness conditions. The earth flow was triggered on 26 February 1907 and the main movement lasted about one week, involving several buildings, including those of cultural interest. Historical documentary investigations and historical climatological analysis both indicate that the earth flow was triggered by a preceding heavy rain period, which independent historical sources suggest also caused the activation of landslides over a wider area around Montemurro. Currently, the earth flow is NE–SW oriented, extends for a length of ~1.1 km, and has an average width of ~220 m. The landslide is in a dormant activity phase. From a methodological point of view, the research stresses the importance of integrated approaches to investigate natural hazards, particularly by the use of historical data. This research may be of interest to academics, practitioners, and policymakers for both the methodological approach followed and results gained, useful in view of both risk mitigation and territorial planning of landslide-prone areas.

Remote landslide mapping, field validation and model development – An example from Kravarsko, Croatia

The Kravarsko settlement area, in northern Croatia, has multiple landslides and damage to buildings and infrastructure caused by landslides. However, actual landslide investigation data for the wider Kravarsko area (pilot area PA1) is relatively sparse and no landslide inventory or typical landslide model exists. The aim of this research was to develop such a landslide inventory by integrating new approaches in geohazard research such as remote landslide mapping from high resolution digital elevation models (DEMs) and current and historical aerial images with existing and new geological data related to landslides. The conclusion is that detailed DEMs are more than adequate for the development of reliable landslide inventories but field checks are still necessary to account for the specific set of natural and man-made conditions found in the research area. The landslide inventory developed for Kravarsko has been field validated in a smaller validation area (VA1) and a typical simplified landslide model for PA1/VA1 was developed. Within the model, sliding is interpreted as complex with multiple generations of sliding and multiple sliding surfaces. Based on the analysis undertaken and the available field data, around 10-20% of urban structures are endangered in the Kravarsko area and anthropogenic activity was determined as an important landslide triggering factor for landslide activation or reactivation. Still the question remains of how to quantify the anthropogenic influence? The developed landslide inventory for PA1/VA1 could be used for local urban planning/development and endangerment assessment/evaluation.

Evaluating landslide response in a seismic and rainfall regime: a case study from the SE Carpathians, Romania

Abstract. There have been many studies exploring rainfall-induced slope failures in earthquake-affected terrain. However, studies evaluating the potential effects of both landslide-triggering factors – rainfall and earthquakes – have been infrequent despite rising global landslide mortality risk. The SE Carpathians, which have been subjected to many large historical earthquakes and changing climate thus resulting in frequent landslides, comprise one such region that has been little explored in this context. Therefore, a massive (∼9.1 Mm2) landslide, situated along the river Bâsca Rozilei, in the Vrancea seismic zone, SE Carpathians, is chosen as a case study area to achieve the aforesaid objective (evaluating the effects of both rainfall and earthquakes on landslides) using slope stability evaluation and runout simulation. The present state of the slope reveals a factor of safety in a range of 1.17–1.32 with a static condition displacement of 0.4–4 m that reaches up to 8–60 m under dynamic (earthquake) conditions. The groundwater (GW) effect further decreases the factor of safety and increases the displacement. Ground motion amplification enhances the possibility of slope surface deformation and displacements. The debris flow prediction, implying the excessive rainfall effect, reveals a flow having a 9.0–26.0 m height and 2.1–3.0 m s−1 velocity along the river channel. The predicted extent of potential debris flow is found to follow the trails possibly created by previous debris flow and/or slide events.

Research on characteristics and failure mechanism of Guizhou Shuicheng landslide based on InSAR and UAV data

Studying the failure mechanism, process and triggering factors of landslides has important significance for the identification, monitoring and early warning of hidden dangerous landslides in similar environments. On July 23, 2019, a landslide occurred in Shuicheng County, Liupanshui City, Guizhou Province. After the disaster, InSAR, satellite time-series imagery, and UAV emergency surveys were quickly used to study and analyze the natural geographic conditions, characteristics and failure mechanisms of the landslide. Studies have shown that the main triggering factors of the landslide were regional continuous rainfall. Additionally, this disaster was also related to road construction, regional topography and stratum lithology. According to the InSAR results, the landslide area had a weak surface deformation of approximately 5 ​mm per year beginning two years ago. However, this deformation was not sufficient to initiate the landslide. The accumulated rainfall in July reached 372.1 ​mm in the landslide area, and the rainfall on the landslide day was 48.8 ​mm, reaching the level of heavy rainfall. This landslide occurred on a loose basalt weathered residual soil layer that provided optimal geological conditions for the landslide, while the convex bank formed by roadworks provided the optimal topographical conditions. These elements together caused the landslide.

Analysis of plant root properties, texture and porosity that affects landslides in Tangka Sub-Watershed

Tangka Sub-Watershed in West Sinjai District is one of the sub-watersheds in South Sulawesi, which often experiences landslides. Factors causing landslides were divided into control factors and trigger factors. One of the triggering factors for landslides is the characteristics of soil and plant properties. Several previous research results stated that soil characteristics greatly affect the occurrence of landslides, while plant roots help stabilize soil bonds and slopes. This study aims to determine the triggering factors of the soil (soil texture and porosity) and the distribution of plant roots that affect the occurrence of landslides. Soil texture analysis using hydrometer method. Soil porosity and moisture content using gravimetric and pycnometer methods. The results showed that the soil texture was dominated by silty clay loam impact on low soil cohesion. Intensive land use decreases soil porosity to <50% and increases soil bulk density to 1.4 g/cm3. The soil quickly becomes saturated and could induce the rotting of plant roots. These characteristics increase the vulnerability of the soil, thereby increasing the potential for landslides.

Persistent Scatterer Interferometry for Pettimudi (India) Landslide Monitoring using Sentinel-1A Images

The continuous monitoring of land surface movement over time is of paramount importance for assessing landslide triggering factors and mitigating landslide hazards. This research focuses on measuring horizontal and vertical surface displacement due to a devastating landslide event in the west-facing slope of the Rajamala Hills, induced by intense rainfall. The landslide occurred in Pettimudi, a tea-plantation village of the Idukki district in Kerala, India, on August 6–7, 2020. The persistent-scatterer synthetic aperture radar interferometry (PSInSAR ) technique, along with the Stanford Method for Persistent Scatterers (StaMPS), was applied to investigate the land surface movement over time. A stack of 20 Sentinel-1A single-look complex images (19 interferograms) acquired in descending passes was used for PSInSAR processing. The line-of-sight (LOS ) displacement in long time series, and hence the average LOS velocity, was measured at each measurement-point location. The mean LOS velocity was decomposed into horizontal east–west (EW ) and vertical up–down velocity components. The results show that the mean LOS, EW, and up–down velocities in the study area, respectively, range from –18.76 to +11.88, –10.95 to +6.93, and –15.05 to +9.53 mm/y, and the LOS displacement ranges from –19.60 to +19.59 mm. The displacement values clearly indicate the instability of the terrain. The time-series LOS displacement trends derived from the applied PSInSAR technique are very useful for providing valuable inputs for disaster management and the development of disaster early-warning systems for the benefit of local residents.

Artificial intelligence approaches for spatial prediction of landslides in mountainous regions of western India

The prediction of landslide is a complex task but preparing the landslide susceptibility map through artificial intelligence approaches can reduce life loss and damages resulting from landslides. The purpose of this study is to evaluate and compare the landslide susceptibility mapping (LSM) using six machine learning models, including random forest (RF), deep boost (DB), stochastic gradient boosting (SGB), rotation forest (RoF), boosted regression tree (BRT), and logit boost (LB) in the mountainous regions of western India. The landslide inventory map consisting of 184 landslide locations has been divided into two groups for training (70% data set) and validation (30% data set) purposes. Fourteen landslide triggering factors including slope, topographical roughness index, road density, topographical wetness index, elevation, slope length, drainage density, stream power index, geomorphology, rainfall, soil, lithology, lineament density, and normalized difference vegetation index have been considered using the boruta approach for the LSM. The results reveal that the RF model has the highest precision in terms of area under curve (0.88; 0.89), kappa (0.62; 0.50), accuracy (0.81; 0.77), and specificity (0.86; 0.86) both in the study region and secondary region, respectively. Hence, it can be concluded that the RF is an effective and promising technique as compared to DB, SGB, RoF, BRT, and LB for landslide susceptibility assessment in the research area as well as in regions having similar geo-environmental configuration.

Cracking Modeling In Partially Saturated Media With Phase-Field Method And Application To Rainfall-Induced Landslides

Cracking is the principal mechanics of failure in engineering structures and geo-logical systems. Complex cracking patterns are observed in rock-like materials under compressive stresses. On the other hand, rainfall is one of the main triggering factors of landslides. Therefore, the rainfall induced landslides should be investigated by considering complex relationships between rainfall and rising groundwater table and related reduction of shear strength. In this paper, we present a new phase-field method for modeling the transition from diffuse damage to localized cracks. The damage variable is assumed to be driven by the deviatoric strain and positive (or dilatant) volumetric strain. This allows the description of tensile, shear and mixed cracks. The phase-field method is extended to partially saturated porous media by considering hydromechanical and phase-field coupling mechanisms. The proposed phase-field method is then applied to the analysis of rainfall-induced landslides in partially saturated conditions. The evolutions of pore water pressure, displacement and damage fields are predicted. Different failure mechanisms are discussed.

Pore water pressures and hydraulic conductivity in the slip zone of a clayey earthflow: Experimentation and modelling

Rainfall is among the most relevant triggering factors of landslides. With the aim of defining a model of the influence of rainfall on deep landslides, this paper presents data of a long-term monitoring of pore water pressures in a clayey earthflow, and the results of many field permeability tests carried out with several procedures. Pore water pressures are being monitored in the Costa della Gaveta slope since 2005. In the last years, new instruments have been installed and permeability tests have been carried out in the stable formation, in the landslide body and in the slip zone. The hydraulic conductivities of the three different zones of the subsoil have been evaluated by two types of field tests: falling head tests (by Casagrande piezometers and test wells), and localized constant head tests (by a permeameter ad hoc designed). The experimental data show that: a) pore water pressures along the slip zone respond to rain more than in the landslide body and in the stable formation, even at about 25 m depth; b) the hydraulic conductivity ksz of the slip zone is much higher than that of the landslide body, kl, in turn higher than the hydraulic conductivity of the stable formation, kf. The numerical 3D modelling of steady-state reference conditions and of transient processes caused by the historical rainfall series shows that the occurrence of ksz higher than kl and kf determines an overall drainage effect which makes the global limit equilibrium safety factor SF greater than that evaluable for other possible conditions. On the other side, even the lowest measured hydraulic conductivity ksz provides seasonal rain effects on water pressures along the slip surface such to determine significant SF variations in phase with the observed seasonal displacement rates.

Landslide susceptibility mapping using integrated approach of multi-criteria and geospatial techniques at Nilgiris district of India

Nilgiris district of Tamil Nadu, India, is one of the landslide-prone zones. In recent years, this district has suffered a lot from landslides. A study was done using the geospatial technology–based analytical hierarchical process (AHP) model, a multi-criteria approach integrated with representation operating characteristics (ROC)–area under curve (AUC) validation technique to map the landslides susceptible zones of this district. The weights of the landslide triggering (or influencing or inducing) factors (criteria) obtained the AHP model are slope (0.377), geology (0.281), distance from the faults (0.119), distance from the drainage (0.071), rainfall (0.062), Land Use Land Cover (LULC) (0.039), elevation (0.027), and slope aspect (0.023). The obtained weights of respective influencing factors of this region illustrate their priorities in the order from very high to very low with a Consistency Ratio (CR) value equal to 0.07 (< 0.1) that indicates the acceptability of the weightage. Resulted landslide-susceptible (potential) zones of this district have revealed that 22.65% of the study area is very low susceptible, whereas 20.17%, 15.88%, 13.32%, and 27.98% of the regions are low, medium, very highly vulnerable to landslides, respectively. It was also found that the Udagamandalam block (north-central part of the Nilgiris district) is the most susceptible to landslides where Gudalur (north-west) is the least susceptible block. However, Coonoor (south-east) and Kotagiri (north-east) have acquired the second and third places, respectively. The empirical and binormal ROC curve estimates the accuracy nearly equal, i.e., 75.35% and 75.36%, respectively, that signifies the good performance of the AHP model in this region. Obtained landslide susceptible map of Nilgiris can be further used by the local or state or central authorities to make the policies and planning for the sustainable development of the region.

The impact of ground-ice thaw on landslide geomorphology and dynamics: two case studies in northern Iceland

As consequence of ongoing climate change, permafrost degradation is thought to be increasingly affecting slope stability in periglacial environments. This is of growing concern in Iceland, where in the last decade, permafrost degradation has been identified among the triggering factors of landslides. The role of ground ice in conditioning the morphology and dynamics of landslides involving loose deposits is poorly understood. We show the geomorphological impact of the Móafellshyrna and Árnesfjall landslides that recently occurred in ice-cemented talus deposits in northern Iceland. Using field and aerial remote-sensing measurements of the morphological and morphometric characteristics of the landslides, we assess the influence of thawing ground ice on their propagation style and dynamics. The two mass movements are complex and are similar to rock- and debris-ice avalanches, changing trajectory and exhibiting evidence of transitioning their style of motion from a dry granular mass to a debris flow-like movement via multiple pulses. We infer that the thawing of ground ice together with the entrainment of saturated material provided the extra fluid causing this change in dynamics. The hazardous consequences of permafrost degradation will increasingly affect mountain regions in the future, and ground-ice thaw in steep terrain is a particularly hazardous phenomenon, as it may induce unexpected long-runout failures and can cause slope instability to continue even after the landslide event. Our study expands our knowledge of how landslides develop in unstable ice-cemented deposits and will aid assessment and mitigation of the hazard that they pose in Iceland and other mountainous periglacial areas.

A comparative machine learning approach to identify landslide triggering factors in northern Chilean Patagonia

Worldwide landslides correspond to one of the most dangerous geological events due to their destructive power and unpredictable nature. In the Chilean Patagonia, the SERNAGEOMIN (Chilean Geological Survey Service) has detected 2533 landslide events in the Northern Patagonia (42. 7∘S, 72. 4∘W) alone, a small area compared to the whole Chilean Patagonia. However, only 11 evens have known date. Consequently, it is not possible to associate temporal triggers and mechanisms that control such events, resulting in a lack of understanding of the factors that enable landslides. This work aims to detect landslides and identify the main environmental variables (climatic and geomorphological) that explain their occurrence using machine learning methods. We will address the following research questions: 1) How can a temporal landslide dataset be built using Landsat images and Google Earth Engine in Northern Patagonia? 2) Once the landslides and their timing have been detected, what are the main variables that condition the landslide processes? In our work, we developed a temporal dataset of landslides for the northern Patagonia of Chile. We used three machine learning approaches, where it was possible to identify the main environmental variables that allow us to predict their generation. Statistical models show that during the last 19 years, there has been complex interaction between different environmental variables that have influenced the activity of landslides. Climatic indices, indicators of extreme events, have a high incidence in the events’ predictive capacity. However, the most important are those linked to Patagonia’s tectonic context. In particular, the time elapsed after the eruptive event of the Chaiten volcano. Finally, the Liquine-Ofqui Fault System’s presence extending throughout the entire north of Patagonia has generated discontinuities at a general level, causing significant geomorphological instability. The study area has a relief in evolution and reactive to climatic conditions. Therefore, we highlight the need to understand better the interaction between geological and climatic processes and the future impact of these natural hazards. We emphasize the importance of analyzing landslide controls, considering both geomorphological variables and sporadic geological events.

Landslide Susceptibility Assessment Using Bivariate Method: A Case Study from River Neelum and Jehlum Catchment Area

Landslide is a frequently occurring natural calamity in the northern areas of Pakistan. The current study is aimed to assess the susceptibility of landslide hazard to highlight the vulnerable areas for the purpose of risk reduction along Neelum and Jehlum rivers in district Muzaffarabad. A data-driven predictive approach was adopted to conduct this study by using Weight of Evidence (WOE) model along with eleven conditioning factors. A spatial distribution map of landslides was prepared using orthophoto, previous records, and derivatives (hill shad, topographic openness, slope, aspect, curvature) of Digital Elevation Model (DEM). The results show that the roads, lithology, and rivers are the most important triggering factors for landslides in both valleys. Approximately 30% of the area is under low susceptibility zone in Jhelum valley while only 13% of the area falls under low susceptibility zone in Neelum river valley. In Jhelum river valley the medium susceptibility zone covers 35% of the total area whereas, Neelam river valley has 26% of the total area under medium susceptibility zone. Around 61% of the land in the Neelam river valley and 35% of the land in the Jhelum river valley are under high susceptibility zone. The area under high hazard lies in the north-east of the district due to multiple conducive factors to trigger landslides including weak lithology (mudstone, sandstone, shales, and clays), high altitude along steep slopes and excessive precipitation (1800 mm/ year). Furthermore, the high hazard zone in study area is not suitable for construction purpose but was suitable for plantation. The validation result (89.41%) is justifying the performance of this model.