ABSTRACT Landslides have emerged as one of the most devastating geological hazards in the Himalayan belt, with a noticeable rise in both frequency and magnitude in the past few decades. It poses a critical threat to human life and public assets, creating an urgent need to assess landslide risk in these vulnerable mountainous areas. This study presents an in‐depth investigation of landslides hazard, vulnerability, and associated risk in Darma Valley, Kumaun Himalaya, India. To assess landslide susceptibility, four machine learning algorithms namely, Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Bootstrap Aggregating (BA) were employed. Model performance was estimated by utilising ROC–AUC curve. Among them, the RF model showed the highest predictive accuracy and was used to integrate rainfall and seismic intensity maps to generate rainfall‐induced, seismic‐induced, and combined landslide hazard maps. The vulnerability was assessed using land‐use/land‐cover classes and their associated monetary values. The analysis revealed roads, followed by settlements and dam structures, as the most vulnerable elements due to their high reconstruction costs and exposure to landslides. The risk map is obtained by integrating the combined hazard with the vulnerability. It shows that about 9% of the area lies in high‐to‐very high risk zones, 22% in moderate risk zones, 26% in low risk zones, and 43% in very low risk zones. Societal risk assessment reveals that ~ 58% of the residents are settled in high to very high risk areas. These findings can support sustainable development and safer urban planning in the Himalayas by enhancing decision‐making processes.

Susceptibility, vulnerability, and risk assessment (SVRA) related to landslides of the existing Himalayan township are essential for land-use planning of the area. Therefore, in the present study, a detailed SVRA of the Nainital township in the Lesser Himalaya, India, constituting thirteen municipal wards, has been carried out. Three machine learning approaches, such as Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM), were used for the preparation of landslide susceptibility maps. ANN has a slightly higher success and prediction rate accuracy and is used for landslide risk assessment of the area. Intersecting different elements at risk with the landslide susceptibility map, the landslide risk assessment of the area has been evaluated at the local municipality ward level. It exhibits that 44

The Dharali debris flow on 5 August 2025, in Uttarakhand, India, was a catastrophic disaster that occurred in Kheer Gad, a tributary of the Bhagirathi river. It claimed approximately 60 lives and covered an estimated 3 hectares of apple orchards under debris. The damage also included at least 25-30 animals and a market comprising 65 hotels, over 30 resorts, and homestays. The present study involved the geomorphic characterisation of Kheer Gad to assess the debris flow potential of the catchment and debris flow simulation to ascertain the source, potential flow dynamics, and reconstruction. Findings reveal that the 17 km2 Kheer Gad catchment is inherently unstable, as suggested by Melton's ruggedness number of 0.8, significantly above the 0.6 debris-flow threshold. The trigger was not a single event, but an antecedent rainfall of similar to 195 mm/30 days, which saturated glacial and landslide-derived source materials. Debris flow simulation revealed 60 kPa flow pressure, velocities of 5-10 m/s, flow height of 5-10 m, spread area of similar to 18 hectare, and volume estimate of 995,580 +/- 200,000 m3-1,285,260 +/- 126,000 m3. These values are validated against field observations. Notably, the disaster was exacerbated by human vulnerability, given the doubling of built-up structures between 2011 and 2025, despite prior warnings in 2013. The present study provides a transferable methodology for assessing similar high-risk, glaciated basins to prevent such avoidable disasters.

The Himalayas comprise a wide diversity of rock types with variations in the rock mass’s inherent textural characteristics and structural features. This terrain has been explored frequently for numerous rock engineering projects and is witnessing progressive infrastructure development owing to the potential of tourism and the prospect of producing hydroelectricity. Moreover, the instability of rock masses continues to be a cause of concern in the region. Therefore, the insight into the physico-mechanical properties of the Himalayan rocks has significant implications for determining appropriate support systems and ensuring cost-effective, sustainable and efficient construction of various structures such as dams, bridges, underground openings, or rock excavations and for optimising the drilling and blasting parameters. Over the period, several researchers have evaluated the engineering properties of the Himalayan rocks from different perspectives. However, a brief review of the variation of rock properties for different tectonostratigraphic zones through the available literature is somewhat elusive to date. In line with this, the present study focused on an in-depth analysis of this issue by scrutinising and assessing accessible research articles on the engineering characteristics of Himalayan rocks. Secondly, possible avenues for further investigations have been presented to provide insight to researchers by identifying the scope and potential of future research in the Himalayan region. This review article can serve as a reference for geotechnical practitioners seeking insights into the properties of rock materials across various rock types in the region.

The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the litho-tectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77％of the landslides lie in very high,20.32％in high,21.29％in medium,and 11.61％in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosion-prone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.

Landslide is one of the most common occurring natural disasters in the Himalayan terrain due to its rugged topography, steep slopes, and structural instability. The repercussions of landslides in Himalaya are often devastating, leading to loss of life, property, and infrastructure. Therefore, it is important to monitor landslides and reduce its consequences for which the state-of-the-art Persistent scatterers-Interferometric Synthetic Aperture Radar (PS-InSAR) technique is readily used nowadays. The present study illustrates a combined approach using PS-InSAR and a semi-quantitative empirical model for landslide risk micro-zonation utilizing the case study of Solang village (Himachal Pradesh, India). The analysis exhibits that a large part of the village is undergoing deformation with a subsidence rate of upto 80 mm/year near the crown portion of the landslide. The risk analysis indicates that 50

Landslides rank among the most devastating geological hazards in mountainous areas. One such region in Himalaya is the Upper Beas Valley in Himachal Pradesh, India having history of number of destructive landslides. Therefore, landslide susceptibility maps (LSM) become important tool for future land use planning and infrastructure development of the area. In the present study, two quantitative methods namely weight of evidence (WoE) which is a bivariate statistical method and state-of-the art artificial intelligence method namely random forest (RF) were compared for the preparation of LSM of the area. Landslide inventory and various conditioning factor including elevation, slope aspect, slope angle, lithology, plan curvature, profile curvature, distance to road, distance to thrust, distance to drainage, topographic wetness index and rainfall are prepared using remote sensing satellite images and extensive field survey. The prepared dataset was randomly divided into 70:30 as a training dataset and validation dataset, respectively. The RF model was trained repeatedly using training data till the optimized results are obtained. For WoE method, relative weights of each class of conditioning factors were calculated using weight of evidence statistics. Subsequently, cumulative weight of each factor was used for the preparation of susceptibility map. The susceptibility maps created indicate that approximately 40–43

The NW Himalaya has been subjected to frequent disastrous landslides of different types owing to frequent extreme rainfall events and rock mass shearing caused by structural and/or lithological contrast. Though majority of the landslides in the NW Himalaya are of complex type comprising debris and loose rock mass that may result into debris flow and/or rockfall, their potential behavior is rarely explored. The present study aims to evaluate the recurrence of one such complex landslide (0.23 Mm2) of Yamuna Valley, NW Himalaya that is subjected to rock mass shearing and the region accommodating this landslide receives frequent extreme rainfall events. A huge slope failure in this landslide occurred on 12 September, 2017 damaging a 400 m stretches of the National Highway (NH) road. The landslide location has strategic significance, since up to 0.3–0.4 million pilgrims travel annually on the road passing through the landslide slope. To evaluate the potential behavior of landslide and to understand the factors causing this landslide (pre-failure analysis), slope stability analysis and rockfall simulation were performed. Pre-failure analyses indicated that the maximum shear strain of 0.14–0.18 and total displacement of 2–8 m likely developed parallel to the slope. The possibility of rainfall triggering is explored in view of increasing rainfall, soil moisture, and surface runoff conditions. Tectonic influences are also evaluated using joints and fracture patterns in rock mass. Post-failure analysis showed that though the maximum shear strain and the total displacement had reduced to 0.07–0.15 and 2–5 m, respectively after the failure, the slope is still unstable. Rockfall simulation revealed the potential for rockfalls having energy and velocities in the range of 900–4000 kJ and 18–75 m/s, respectively.

In the present study, the influence of visible rock textures on the durability and strength characteristics of different rocks viz. shale, slate, phyllite, gneisses, granites, schists, metabasics, dolostone and quartzites were investigated. A total twenty seven rock blocks with distinct and visible textural features collected from the Himalayan terrain were studied for the purpose. The minerological and textural features of rocks were studied under optical microscope. The slake durability index (Id) and unconfined compressive strength (UCS) through indirect test i.e. point load test for each group of rock types were determined in the laboratory using standard ISRM suggested methods. It has been observed that rocks located near the major geological thrusts and faults have undergone deformation because of the tectonic activity around these faults and thrusts, and thus resulted either in reduction in grain sizes or mylonitization and exhibits exceptionally higher UCS in rocks like granite and dolomite contrast to rocks located away from the thrust and fault whereas in amphibolites, the lower UCS is due to development of schistosity in amphibolites near fault/thrust. The rocks with higher mica or clay contents disintegrate faster and having lower strength values. It has been observed that the mineral contents of the rock influence their durability rather than textures.

This article assesses the causes behind the ongoing subsidence-induced instabilities in Dar village, which is situated over deposits of palaeo-landslides in Kumaun Himalaya. A detailed field investigation has been carried out in and around the study area to identify the indications of subsidence there and to understand the factors responsible for the activation of the same. Subsequently, the geotechnical characterisations of overburden slope material and numerical slope stability assessment have been performed to substantiate the field observations. It has been found that the village is situated on a slope comprising weak material 90

Although point load strength is considered as a best proxy for uniaxial compressive strength and also incorporated in the routinely used rock mass rating (RMR) system, the effects of temperature treatments on the point load strength has not gained ample attention over the years. Accordingly, in this investigation, two different cooling techniques (i.e. water- and air-cooling methods) has been used in order to study the influence of different heating–cooling treatments on the physical properties, microstructural characteristics and point load strength of Himalayan granite collected from Sangla valley, Himachal Pradesh. The temperatures for heat treatment were targeted at 100 °C, 200 °C, 300 °C, 400 °C, 500 °C and 600 °C. As a response to thermal treatments, increase in effective porosity, decrease in density and increase in damage coefficient occurs which causes exponential decrease in point load strength. It decreases as high as 74

Identification of landslide susceptible zones is the preliminary step to plan mitigation measures in landslide-prone mountainous terrains. The use of various machine learning (ML) algorithms has proven their superiority in terms of enhancing the success rate in susceptibility studies. Therefore, the present study focuses on spatial prediction of landslides using integrated supervised and unsupervised machine learning (ML) techniques with reference to Bhagirathi Valley, NW Himalaya. A landslide inventory of 514 landslides and 14 viable causative factors of landslides in the study area have been selected for the analysis. Three efficient supervised ML techniques, i.e., random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbour (KNN), have been integrated with an unsupervised ISODATA cluster classification technique to prepare the landslide susceptible maps (LSM) of the study area. All the models depict that the greater part of the high and very high landslide hazard zones lie in the Main Central Thrust zone and its vicinity in the Bhagirathi Valley. The accuracy of each model was determined and compared using several statistical signifiers like sensitivity, specificity, area under curve, accuracy, and Kappa index. The results show that XGBoost and RF models exhibit higher performance accuracy than KNN. The quantitative assessment of prepared LSMs of the study area was also done using frequency ratio (FR) and frequency density (FD). The results indicate the consistency of each model in the prediction of landslide zones in the study area as FR and FD both increase with the increase of landslide susceptibility levels from very low to very high in all the models.

Landslide is one of the most destructive hazards in the Upper Beas valley of the Himalayan region of India. Landslide susceptibility mapping is an important and preliminary task in order to prospect the spatial variability of landslide prone zones in the area. As the use of machine learning algorithms has increased the success rate in susceptibility studies, the performance of the four machine learning models, namely Naïve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were initially tested for landslide susceptibility mapping in the area. Landslide inventory containing both landslide and non-landslide data and thirteen landslide conditioning factors were considered to train the models. The models were optimized using hyperparameter optimization and input factors selection based on variable importance. Among the four models, Extreme Gradient Boosting (XGBoost), an advanced ensemble-based machine learning algorithm, demonstrated superior performance (AUC = 0.91) followed by RF, NB and KNN with AUC values of 0.88, 0.87, and 0.82. Therefore, XGboost model was selected for detailed study, including sensitivity analysis. The results depict that 44

Various geomorphic indices and climatic proxies coupled with field investigations has been used to obtain the quantitative measurement of an active tectonic landscape and could infer the basic information necessary for estimating long-term deformation as well as landform evaluation in tectonically active mountain belts. The evaluation of inter-relationship between geomorphic indices and climatic proxies with spatial distribution of landslides in an area would be useful for hazard assessment and mitigating the landslide risk. The present study aims to investigate the inter relationship between geomorphic indices and climatic proxies with landslides in the Bhagirathi River valley, NW Himalaya. Accordingly, a detailed landslide inventory consisting of 514 landslides and various geomorphic indices in the study area were prepared using high resolution satellite images and field visits. Subsequently, the statistical analysis of landslides frequency with each of the geomorphic and climatic parameter was assessed to understand their role in the spatial distribution of landslides in the study area The results indicate that the relative higher tectonic activity as evident from moderate to low Vf ratios, high value of KS, presence of Knick points, V-shaped valley and rocky jointed and barren slopes attribute towards the occurrence of rockfall in Upper Himalayan Crystalline zone. Whereas, in MCT zone, high relative active tectonics apparent from the presence of knickpoints, lowest average Vf ratio and highest average KS values and high rainfall causes landslides. The Garhwal region is highly dissected indicating fluvial erosion and toe cutting and high erosion rates due to presence of loose debris and confined in high rainfall zone leads to numerous small mass movements in this zone.

Four bivariate methods viz frequency ratio, weight of evidence, Yule’s coefficients and information value were utilized for the preparation of the landslide susceptibility map of the hilly township of Mussoorie. Two scenarios, one with partitioning landslide inventory prepared till 2019 with 70% landslides, and another with all the active landslides till 2019 were used for the preparation of landslide susceptibility maps. In order to understand the efficacy and the reliability of each of the bivariate approach used under both the scenarios, the maps thus obtained were overlaid with the landslides that occurred in the area during excessive rainfall of 2020. It has been noted that the landslide susceptibility maps prepared using four different bivariate methods exhibit more or less similar results, nevertheless of all the four methods used, information value method indicate that more than twice the area (∼38%) fall in high and very high landslide susceptible zones in scenario-I. The scenario-II exhibits higher percentage of area falling in high and very high landslide susceptible zones for all the methods as compared to scenario-I, still the Information value indicate the highest percent of area (∼31%) falling in the high and very high landslide susceptibility zones. The validation of the maps prepared using scenario-I exhibit 58–75% of the 2020 landslides occur in high and very high landslide susceptibility zones, whereas in scenario-II, 57–72% of the 2020 landslides falls in high and very high landslide susceptibility zones. Finally the weight of evidence method and information value method indicate the higher prediction accuracy under both the scenarios

Earthquake-induced landslide hazard is the most serious threat in seismo-tectonically active mountains like the Himalayas. It has frequently been noted that the damage caused by earthquake-induced landslides is significantly greater than the earthquake itself. Therefore, assessing the susceptible zones of earthquake-induced landslides in seismically active areas is essential. In this study, the probabilistic hazard assessment of the earthquake-induced landslides has been conducted for the Goriganga Valley, Kumaun Himalaya. Numerous studies indicate that a great earthquake of magnitude 8 Mw or higher could strike this area at any time. Hence, mapping earthquake-induced landslides using an improved Newmark's model has been conducted for earthquakes of magnitude 8 Mw. The inclusion of arias intensity to estimate the permanent displacement of the slope for future scenario earthquakes make this work unique from others. The model provides the permanent displacements of potential slopes, which is the function of shear strength parameters of jointed rock mass, the inclination angle of valley slopes, and the arias intensity of the area. It provides the spatial distribution of possible slope failures in the area. It has been noted that ~25% of the study area is susceptible to earthquake-induced landslides when subjected to direct shaking of an earthquake of magnitude 8 Mw. The results of this work provide great insight to planners and civil engineers for hazard mitigation and assessment of the study region.

The Himalayan states are familiar with the adverse impact of extreme events like excessive precipitations, which lead to floods, flash floods, landslides, debris flow, avalanches, etc. hence the disasters. The Uttarakhand (UK) state in the Himalaya experiences a great loss of lives, livestock, properties, and natural wealth due to such calamities. On the night of 19 August 2022 (10:15 PM) and early morning of 20 August 2022 (02:15 AM), heavy rain together with a cloud burst triggered flash floods in the Maldevta area of the Dehradun district, which led to large-scale destruction of the area. A prolonged heavy downpouring on 20 August 2022 at Maldevta and surrounding areas damaged the channel and bank of the Song and Baldi rivers for a 15 km stretch between Sarkhet and Thano, and completely washed away Sarkhet (807 m asl), Kumalta (787 m asl) and lower portion of Maldevta town (723 m asl).

Seismic anisotropy in the crust beneath the Satluj valley and the adjoining region of the northwest Himalaya has been studied with the help of shear wave splitting analysis of P-to -S or Ps converted phases originating at the crust-mantle boundary. A total of 144 splitting parameters (phi, 8t) have been computed from 130 teleseismic earthquakes recorded by 13 broadband seismological stations spanning from the Lesser Himalaya to Tethyan Himalaya passing across the Satluj valley region. The predominant NW-SE fast polarization directions (FPDs) in the Lesser and Higher Himalaya follow the strike of surface geological features suggesting structural anisotropy. The NW-SE oriented FPDs in the Tethyan Himalaya fairly coincide with the regional extensional strain. The presence of such extensional strain within the crust might cause Lattice Preferred Orientation (LPO) of aniso-tropic minerals resulting in observed anisotropy. The large strength of anisotropy (8t: 0.15-0.80 s) suggests a primary contribution of anisotropy from the middle and lower crust. These observations support the assumption that the deep crust in the study region has undergone widespread and relatively uniform strain in response to crustal shortening and E-W extension.

We have mapped more than 400 major landslides (debris slides, rockfalls, and rock avalanches) in 5 fluvial valleys in Himalaya (India) between 77.3° E - 80.5° E longitudes. Field/high- resolution satellite imagery based landslide area mapping and field based landslide thickness approximation were used to determine landslide area and volume. Area-volume scaling exponents of these landslides revealed a lateral variation in the study area implying that landslide slopes in the eastern part of the study area retain relatively less volume that increases towards western part of the study area. We have hypothesized that such lateral variation is possibly caused by lateral variation in the landslide occurrence that in turn is mostly caused by lateral variation in the seismic-climatic regimes. Following the hypothesis, we noted that rainfall, surface runoff, soil moisture, and air moisture (climatic variables) data of years 1982-2020 represent a general decrease laterally from east to west in the study area. Further, the role of topography on the climate variables is also noted as it increases from east to west. Earthquake (Mw=>4) distribution (1960-2020), Arc Parallel Gravity Anomaly (APGA), cumulative seismic moment, shear stress accumulation rate, and convergence (India-Eurasia) rate (Seismic variables) also represent a general decrease laterally from east to west in the study area. The climatic variability is attributed to the spatial variability of the Indian Summer Monsoon (ISM), whereas seismic variability is referred to the spatial variability in the subsurface pattern of the Main Himalayan Thrust (MHT). Thus, such variability in the seismic-climatic regimes is noted to support our hypothesis.

In the present study, inner Kumaun Himalayan region between village Jauljibi and Dobaat along the Kali river has been studied to understand the causes and the distribution of landslides in the area. Various geomorphic indices such as longitudinal and topographic swath profile, stream length gradient index, steepness index, and valley floor width to valley height ratio have been extracted for interpreting the active tectonics of the region, and the topographic bedding plane intersection angle (TOBIA) index for the disposition of the bedding or foliation planes of the rocks with respect to the terrain. In addition, the active nature of the thrusts and faults in the region has been interpreted with the offsets or deflection of the river terraces along these thrusts and faults. Interrelationships among these indices and the spatial distribution of landslide indicate that the region between Dharchula and Dobaat is the most tectonically active region as indicated by the presence of Dharchula Fault, Lasku Fault and South Chhiplakot Thrust along with the abnormally high steepness index and knickpoints in the region. Other regions of active tectonics are Dhap—Kalika and Jauljibi—Baniyagaon regions due to Ghatibagarh Kalika Fault, and Berinag Thrust and Rauntis Fault, respectively. In addition it has been reported that the density of landslides in the cataclinal slopes is higher as compared to landslides in the orthoclinal and anaclinal slopes, indicating the role of the disposition of the rocks with respect to the terrain in the spatial distribution of landslides in the study area.