Landslide Triggering Research Articles

Age of the late Holocene Bonneville landslide and submerged forest of the Columbia River Gorge, Oregon and Washington, USA, by radiocarbon dating

AbstractThe late Holocene Bonneville landslide, a 15.5 km2 rockslide-debris avalanche, descended 1000 m from the north side of the Columbia River Gorge and dammed the Columbia River where it bisects the Cascade Range of Oregon and Washington, USA. The landslide, inundation, and overtopping created persistent geomorphic, ecologic, and cultural consequences to the river corridor, reported by Indigenous narratives and explorer accounts, as well as scientists and engineers. From new dendrochronology and radiocarbon dating of three trees killed by the landslide, one entrained and buried by the landslide and two killed by rising water in the impounded Columbia River upstream of the blockage, we find (1) the two drowned trees and the buried tree died the same year, and (2) the age of tree death, and hence the landslide (determined by combined results of nine radiocarbon analyses of samples from the three trees), falls within AD 1421–1455 (3σ confidence interval). This result provides temporal context for the tremendous physical, ecological, and cultural effects of the landslide, as well as possible triggering mechanisms. The age precludes the last Cascadia Subduction Zone earthquake of AD 1700 as a landslide trigger, but not earlier subduction zone or local crustal earthquakes.

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.

Zonation of Landslide Hazards Based on Frequency Ratio Modeling at Coonoor and Kothagiri Taluks of Nilgiri District, Tamil Nadu, India

A frequency ratio model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. The Nilgiri Hills (Mountains) of Tamil Nadu are prone to landslides; these landslides are typically the result of the structural failure of thick laterite soils that have been saturated by heavy rains during the monsoon season. The GIS based analysis in the present work incorporates the frequency ratio values from the existing landslides. The landslide inventory of paleoscars was located by a collection of existing historical reports. Detailed field surveys have been carried out in the study area. In the observation, 118 landslide locations were identified and mapped by GIS; out of that, 83 (70 %) locations were randomly selected for the training data set and the remaining 35 (30 %) cases were used for the model validation purposes. For spatial modeling, the landslide triggering parameters include active-passive slope, concave-plain-convex slope, drainage density, dissected-undissected slope, geology, geomorphology, lineament density, lineament frequency, lineament intersection density, land use / land cover, rainfall, regolith cover, shallow-moderate-steep slope, soil and water level for landslide hazard zonation. This model has been used to spatially classify the study area into three zones viz; high, moderate and low landslide hazard zones. The frequency ratio model was validated with the existing landslide location data using R-index method. The validation of the results showed that the values is 1115.23 in R-index as high hazard zone when R-index increases with the level of susceptibility. The obtained results will provide a basis for planning, risk reduction strategies and various developmental activities in the Nilgiri area.

Hybrid stochastic-mechanical modeling of precipitation thresholds of shallow landslide initiation

Numerous early warning systems based on rainfall measurements have been designed to forecast the onset of rainfall-induced shallow landslides. However, their use over large areas poses challenges due to uncertainties related to the interaction among various controlling factors. We propose a hybrid stochastic-mechanical approach to quantify the role of the hydro-mechanical factors influencing slope stability and rank their importance. The proposed methodology relies on a physically based model of landslide triggering and a stochastic approach treating selected model parameters as correlated aleatory variables. The features of the methodology are illustrated by referencing data for Campania, an Italian region characterized by landslide-prone volcanic deposits. Synthetic regional intensity-duration (ID) thresholds are computed through Monte Carlo simulations. Several key variables are treated as aleatoric, constraining their statistical properties through available measurements. The variabilities of topographic features (e.g., slope angle), physical and hydrological properties (e.g., porosity, dry unit weight γd, and saturated hydraulic conductivity, Ks), and pre-rainstorm suction are evaluated to inspect its role on the resulting scatter of ID thresholds. We find that: (1) Ks is most significant for high-intensity storms; (2) in steep slopes, changes in pressure head greatly reduce the timescale of landslide triggering, making the system heavily reliant on initial conditions; (3) for events occurring at long failure times (gentle slopes and/or low-intensity storms), the significance of the evolving stress level (through γd) is highest. The proposed approach can be translated to other regions, expanded to encompass new aleatory variables, and combined with other hydro-mechanical triggering models.

Impact of Groundwater Table Fluctuation on Stability of Jointed Rock Slopes and Landslides

Groundwater level plays an important role in triggering landslides. In this paper, Distinct Element Method is used to investigate the impact of groundwater table fluctuation on the stability of jointed rock slopes. For this purpose, 110 cases including different number of joint sets, joint friction angles, joint spacings, and joint angles are considered and the influence of changing groundwater level on the stability of a jointed rock slope is investigated through a series of parametric studies. This study shows that the factor of safety for slopes can decrease significantly with increasing the groundwater level, and the impact is more significant on slopes with steeper joints. Furthermore, as the spacing of the joints decreases, the impact decreases. However, as the joint spacing increases, the groundwater table should rise to a higher elevation to be able to have an impact. Moreover, the impact on the factor of safety is similar for different joint friction angles when the groundwater level elevation is high. This study provides a better understanding of the impact of groundwater table fluctuation on the stability of jointed rock slopes.

Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks

Abstract. In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy) show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow other variables to be easily added, like peak rainfall intensity, with a further performance improvement (TSS = 0.66). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds.

Slope with Predetermined Shear Plane Stability Predictions under Cyclic Loading with Innovative Time Series Analysis by Mechanical Learning Approach.

We propose a mechanical learning method that can be used to predict stability coefficients for slopes where slopes with predetermined shear planes are subjected to cyclic seismic loads under undrained conditions. Firstly, shear tests with cyclic loading of different parameters were simulated on designated slip zone soil specimens, in which the strain softening process leading to landslide occurrence was closely observed. At the same time, based on the limit equilibrium analysis of the Sarma method, the variation of slope stability coefficients under different cyclic loads was investigated. Finally, a Box–Jenkins’ modeling approach is used to predict the data from the time series of slope stability coefficients using a mechanical learning approach. The simulation results show that (1) reduction in coordination number can be an accurate indicator of the level of strain softening and evolutionary processes; (2) the gradual reduction of shear stress facilitates the soil strain softening process, while different cyclic loading stress amplitudes will result in rapid penetration or non-penetration of the fracture zone by means of particulate flow. Although the confining pressure of the slip zone soil can inhibit the increase of fractures, it has a limited inhibitory effect on strain softening; (3) based on field observations of the slope stability factor and stress field, two possible landslide triggering mechanisms are described. (4) Mechanical learning of time series can accurately predict the changing pattern of stability coefficients of slopes without loading. This study establishes a potential bridge between the geological investigation of landslides and the theoretical background of landslide stability coefficient prediction.

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

Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping

Among several natural and anthropogenic conditioning factors that control slope instability, heavy rainfall is a key factor in terms of triggering landslide events. In the Mediterranean region, Crete suffers the frequent occurrence of heavy rainstorms that act as a triggering mechanism for landslides. The Mediterranean island of Crete suffers from frequent occurrences of heavy rainstorms, which often trigger landslides. Therefore, the spatial and temporal study of recent storm/landslide events and the projection of potential future events is crucial for long-term sustainable land use in Crete and Mediterranean landscapes with similar geomorphological settings, especially with climate change likely to produce bigger and more frequent storms in this region. Geoinformatic technologies, mainly represented by remote sensing (RS) and Geographic Information Systems (GIS), can be valuable tools towards the analysis of such events. Considering an administrative unit of Crete (municipality of Rethymnon) for investigation, the present study focused on using RS and GIS-based approaches to: (i) detect landslides triggered by heavy rainstorms during February 2019; (ii) determine the interaction between the triggering factor of rainfall and other conditioning factors; and (iii) estimate the spatial component of a hazard map by spatially indicating the possibility for rainfall-triggered landslides when similar rainstorms take place in the future. Both landslide detection and hazard mapping outputs were validated by field surveys and empirical analysis, respectively. Based on the validation results, geoinformatic technologies can provide an ideal methodological framework for the acquisition of landslide-related knowledge, being particularly beneficial to land-use planning and decision making, as well as the organization of emergency actions by local authorities.

Disproportionate and chronic sediment delivery from a fluvially controlled, deep‐seated landslide in Aotearoa New Zealand

AbstractPast research has highlighted the importance of sediment delivery from multiple‐occurrence regional landslide events triggered by storms or earthquakes. Herein, we examine delivery from a more persistent source of sediment, that of a large, slow‐moving landslide in the soft‐rock hill country of Aotearoa New Zealand. We map and monitor the 80‐ha Rangitikei Landslide from 2015 to 2019 using time‐lapse photography, ground surveys, photogrammetry, and piezometers. We show that the landslide can be divided into several zones with distinctive movement patterns, but all zones respond to river erosion. The fastest zone moves more than 10 m per year in a flow‐like fashion, while other zones move 0.01 m per year via slow sliding. Movement occurs all year round, but is two to three times faster in winter and spring. While rainfall and associated groundwater change are commonly attributed to landslide movement patterns, our data show that river flow correlates closely with the weekly to seasonal variability in movement of the landslide toe. This suggests that fluvial erosion can play an important role in the movement dynamics of highly coupled landslides. We estimate an annual sediment yield to the Rangitikei River of at least 40 000 tonnes, in this first quantification of sediment delivery from an active soft‐rock landslide in Aotearoa. This volume implies 7% of the total catchment suspended sediment yield is derived from 0.03% of the contributing catchment area, demonstrating the disproportionate effect of this (and likely other) deep‐seated landslide(s) as a source of sediment in the Rangitikei catchment. Sediment delivery is more continuous than the episodic supply of multiple‐occurrence regional landslide triggering events, and by delivering mostly fine‐grained sediment, it has a potentially large impact on water quality.

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.

Landslide Hazard Assessment and Zonation by using Slope Susceptibility Evaluation Parameter (SSEP) Rating Scheme- a Case from Debre Sina, Northern Ethiopia

Rainfall-induced landslides of different types and sizes frequently affect the hilly and mountainous terrains of the highlands of Ethiopia. The principal objective of the proposed research study was intended to prepare a landslide hazard zonation map of the area, particularly for hazardous zones. In the present study, the Slope Susceptibility Evaluation Parameter rating scheme has been implemented as a relevant approach to map the landslide hazard of the Debresina area, which has experienced slope failure problems for a long period of time. The geology of the area includes quaternary sediments, ignimbrite, rhyolite, different kinds of basalts, and tuff deposits, which are highly weathered and changed into unconsolidated sediments at some localities. Locally observed geological structures such as joints, dykes, and other discontinuities have a considerable role in the initiation of landslide hazards. As a general methodology, a facet map was prepared from a topographic map (1:50,000) and rating values were assigned to each causative parameter (both intrinsic and external) based on its severity in triggering landslide hazard. The study area was classified into three hazard classes, of which 25 % of the slopes fall into a moderate hazard zone, while 58 % and 17 %were found to be high and very high hazard zones, respectively. Validation of the landslide hazard zonation map with past landslide activities suggests the rationality of the considered governing parameters, the adopted technique, tools, and procedures in developing the study area's landslide hazard map. Further, in order to validate the landslide hazard map prepared during the present study, active landslide activities and potential instability areas, delineated through inventory mapping, were overlaid on it, which yielded promising results.

Impact of Rainfall-Induced Landslide Susceptibility Risk on Mountain Roadside in Northern Thailand

Landslide incidents frequently occur in the upper northern region of Thailand due to its topography, which is mostly mountainous with high slopes. In the past, when landslides happened in this area, they affected traffic accessibility for rescue and evacuation. For this reason, if the risk of landslides could be evaluated, it would help in the planning of preventive measures to mitigate the damage. This study was carried out to create and develop a risk estimation model using the artificial neural network (ANN) technique for landslides at the edge of the roadside, by collecting field data on past landslides in the study areas in Chiang Rai and Chiang Mai Provinces. A total of 9602 data points were collected. The variables for forecasting were: (1) land cover, (2) physiographic features, (3) slope angle, and (4) five-day cumulative rainfall. Two hidden layers were used to create the model. The number of nodes in the first and second hidden layers were five and one, respectively, which were derived from a total of 25 trials, and the highest accuracy achieved was 96.74%. When applying the model, a graph demonstrating the relationship between the landslide risk, rainfall, and the slopes of the road areas was obtained. The results show that high slopes result in more landslides than low slopes, and that rainfall is a major trigger for landslides on roads. The outcomes of the study could be used to create risk maps and provide information for developing warnings for high-slope mountain roads in the upper northern region of Thailand.

Exploring a landslide inventory created by automated web data mining: the case of Italy

Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowdsourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of “landslide events”. This source of information allows continuous feedback from real world, and news concerning landslide events can be rapidly collected. In this work, the newspaper articles about landslides in Italy are automatically collected by an existing data mining algorithm, based on a semantic engine. The news has been analysed to assess their distribution over the territory and to verify the possibility of using them for hazard mapping purpose. In 10 years, from 2010 to 2019, the algorithm identified and geolocated 184322 articles referring to 32525 generical events (“news”). At first, the collected data underwent to a manual verification, followed by a classification based on news relevance, localization accuracy and time of publication. Then, these data have been used to identify the areas and the periods most affected by landslide phenomena. The analyses show that almost 42% of Italian municipalities have been affected by landslide. According to the results, the use of data mining is helpful for the creation of landslide databases where the day and the approximative location (municipality) of the possible landslide triggers are known. This database, in turn, can be used for scientific purposes, as the definition of the meteorological condition associated with landslide initiation, the validation of risk maps. It can also be used for a proper land use or risk mitigation planning, since the most landslide-prone municipalities can be defined.

Landslides induced by the 2017 Mw7.3 Sarpol Zahab earthquake (Iran)

Landslides are the main secondary effects of earthquakes in mountainous areas. The spatial distribution of these landslides is controlled by the local seismic ground motion and the local slope stability. While gravitational instabilities in arid and semi-arid environments are understudied, we document the landslides triggered by the Sarpol Zahab earthquake (November 12, 2017, Mw7.3, Iran/Iraq border), the largest event ever recorded in the semi-arid Zagros Mountains. An original earthquake-induced landslide inventory was derived, encompassing landslides of various sizes and velocities (from rapid disrupted rockfalls to slow-moving coherent landslides). This inventory confirms the low level of triggered landslides in semi-arid environments. It also displays clear differences in the spatial and volumetric distributions of earthquake-induced landslides, having 386 rockfalls of limited size triggered around the epicenter, and 9 giant (areas of ca. 106 m2) active and ancient deep-seated landslides coseismically accelerated at locations up to 180 km from the epicenter. This unusual distant triggering is discussed and interpreted as an interaction between the earthquake source properties and the local geological conditions, emphasizing the key role of seismic ground motion variability at short spatial scales in triggering landslides. Finally, the study documents the kinematics of slow-moving ancient landslides accelerated by earthquakes, and opens up new perspectives for studying landslide triggering over short (~ 1–10 years) and long-time (~ 1000–10,000 years) periods.

Living with Landslides: Perceptions of Risk and Resilience in Far West Nepal

This study presents an analysis of risk and resilience perceptions in two villages of Far West Nepal, Sunkuda and Bajedi, located in the upper Karnali River Basin. The area has been affected by deep-seated and shallow landslides, which have had a devastating impact on many rural lives and livelihoods. While both villages are exposed to landslides, Bajedi is situated in a higher risk zone. Using structured surveys, semi-structured interviews and insights from stakeholder workshops, the risk and resilience perceptions of household residents of the two villages are elicited. The objectives of the study are 1) to understand how residents perceive their risk and resilience to landslides, and 2) to provide evidence and insights on the factors that influence risk perception. Results show that landslides are perceived as an existential risk in both villages, although risks are perceived as more serious in Bajedi. The higher risk perception in Bajedi is shown to be mainly driven by households’ ability to cope, including their energy sources, whether they know whom to call in case of a landslides, as well as household savings and income sources. In Sunkuda, risk perception is shown to be influenced by households’ access to and status of community forests, expenditure on medication, the seasonality and types of water sources, and ethnicity. In both villages, additional drivers of risk perception include households’ social networks, their perceived triggers of landslides, their outstanding loans, and who collects the water. The results demonstrate the interconnectedness of risk perception and indicators of resilience. By elucidating the risk perception of Sunkuda and Bajedi, this study has important implications for local risk management strategies and policies.

Effect of Geological Characteristics on Landslides and Floods in Karangtengah

Bantul regency becomes an area that has a high potential for flooding in 2019 with each percentage of 27%, Bantul regency becomes an area that has a high potential for landslide disasters with a percentage of 43% or there has been a landslide as much as 220 times, landslide-prone points in Bantul districts are in some places there are 5 districts that are very vulnerable, one of the landslides and floods was in the Karangtengah village, research in this Karangtengah to see the most influential geological character as a trigger and cause of landslides and floods in the area of ??Karangtengah, The method used in this research is a field survey method assisted by GIS software, in field survey activities, by looking at the condition of the slopes, lithology on the slopes and structures that develop on the slopes, this analysis is carried out to determine the influence of geology on landslide disasters in the area. the upstream part which is the slope, while the analysis for the flood disaster is in the form of studies including infiltration at several points including residential areas and riverbanks and analysis of rainfall data, infiltration is carried out in the river boar area which is the downstream part, the geological aspects of landslides and floods in the Karangtengah area are quite dominant considering the results of the infiltration analysis, geomorphology and developing structures, besides the geological aspects of hydrometeorology and the use of a fairly large influence on the occurrence of the disaster.

Landslide susceptibility assessment in the Himalayan range based along Kasauli – Parwanoo road corridor using weight of evidence, information value, and frequency ratio

The Himalayan region is prone to landslides. Rainfall-induced slope failure activities in the Indian Himalaya cause considerable damage, posing a serious risk to life and property. This study attributes information regarding landslide triggering parameters to further delineate landslide susceptibility maps of Himachal Pradesh in the Himalayan region. The landslide inventory map was created using information from field visits, Linear Imaging Self-Scanning Sensor (LISS III), and Google Earth. Thereafter, eight landslide causative factors, viz. slope, aspect, curvature, elevation, Landuse Landcover (LULC), soil, lithology, and drainage density were performed by employing the weight of evidence (WOE), information value method (IVM) and the frequency ratio (FR) methods. Using the ArcGIS reclassification tool, the final landslide susceptibility zonation (LSZ) maps were categorized into five susceptibility zones: “very low, low, medium, high, and very high.” The success rate for WOE, FR, and IVM models was determined as 76.27%, 78.20%, and 76.09% respectively, depicting that the FR model based LSZ map is more accurate. According to the FR model based LSZ map, the highly susceptible classes in the case of aspect, curvature, and lithology are southeast, concave, and TBS, respectively. The sparsely vegetated areas are more prone to landslides than the other LULC areas. The higher elevations, ranging from 1191 to 1434, 1434–1655, and 1655–1876 m, are more vulnerable to landslide activities as compared to low elevations. The slope classes 30–45 and 45–60, medium drainage density class and fine loamy class of soils are more likely to landslides. The prepared susceptibility zone map can be used for future mitigation planning in the high and very high susceptibility zones in order to reduce landslide-related human and economic losses.