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In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspace (RSS) ensemble learning techniques for spatial prediction of rainfall-induced landslides in the Uttarkashi district, located in the Himalayan range, India. To do so, a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets. Root Mean Square Error (RMSE) and Area Under the receiver operating characteristic Curve (AUC) were used to evaluate the training and validation performances of the models. The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides. The D-REPT model with RMSE = 0.351 and AUC = 0.907 was identified as the most accurate model, followed by RSS-REPT (RMSE = 0.353 and AUC = 0.898), B-REPT (RMSE = 0.396 and AUC = 0.876), and the single REPT model (RMSE = 0.398 and AUC = 0.836), respectively. The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world. [ABSTRACT FROM AUTHOR]

Landslides are the most common natural disasters in mountainous regions, being responsible for significant loss of life as well as damage to critical infrastructure and properties. As the world population grows, people tend to move to higher locations to construct buildings, thereby making structures vulnerable due to landslides. This paper discusses previous research on the vulnerability assessment of structures exposed to landslides and presents a modified semi-quantitative approach to assess the scenario-based physical vulnerability of buildings based on their resistance ability and landslide intensity. Resistance ability is determined by integrating expert knowledge-based resistance factors assigned to five primary building parameters. Landslide intensity matrix defining different intensity levels is proposed based on combinations of landslide velocity and volume. Physical vulnerability of buildings is estimated and classified as class I, II or III for scenario-based low to very high landslide intensity. Finally, the application of the model is illustrated with a case study of 71 buildings from Garhwal Himalayas, India. [ABSTRACT FROM AUTHOR]

The mountain state of Uttarakhand constitutes a part of the North-western Indian Himalayan region and is inherently vulnerable to natural disasters. It is witnessing faster melting of glaciers and increased occurrences of disasters like cloudbursts, flash floods, landslides, landslide/glacial lake outburst floods, rock–ice avalanches, and debris flows. This study provides a comprehensive assessment of the influence of changing climate on the occurrence of natural hazards. It assesses the status of two major rapidly developing infrastructures in the state (viz., hydropower plants and transportation network) under climate variability. Detailed causative analysis of two major disaster events (in Kedarnath and Chamoli in 2013 and 2021, respectively) and associated destruction is presented to understand the vulnerability of infrastructure and communities to hazards. Based on the challenges identified, a unique two-fold framework is proposed, and recommendations are articulated for building resilient and sustainable mountain infrastructures and communities in the region. [ABSTRACT FROM AUTHOR]

This paper reports stability analysis of active and recurrent Amiyan landslide at Kathgodam in Uttarakhand State, India. The height of the affected slope is approximately 501 m, having two terraces with varying slope inclinations. Lower bench, above the basement, always fails and leads failure paths for others. The problem of slide is further adverse due to fluctuation in water level of Gaula river, which is passing along the slope masses. These rocks mainly belong to Siwalik system suffered by many structural disturbances. The stability assessment of the area is very critical to avoid and manage further damage to surrounding agricultural land as well as population along the river. The slopes' failures increase the siltation, reduce carrying capacity of the river with time and further increase the water level on the bank of the slope. The slope was simulated using two modelling methods, namely limit equilibrium method and finite element method. Results indicate that the stress is mostly confined in the terrace above the basement in all the cases, where the factor of safety is almost unity. The probability of failure estimated between 13.1% and 16.9% using probabilistic slope stability analysis. The results of numerical simulations matched with field observations. [ABSTRACT FROM PUBLISHER]

Mumbai city, having an area of 437 km2 with a population of 12 million, came to a complete halt owing to the unprecedented rainfall of 994 mm during the 24 hours starting 08:30 on 26 July 2005. At least 419 people (and 16 000 cattle) were killed as a result of the ensuing flash floods and landslides in Mumbai municipal area, and another 216 as a result of flood-related illnesses. Over 100 000 residential and commercial establishments and 30 000 vehicles were damaged. The current paper describes Mumbai's drainage system, the details of the flooding and the measures being taken by the city to mitigate such floods in the future. The Mumbai experience would be helpful for planning response strategies for other large cities to cope with similar events in the future. [ABSTRACT FROM AUTHOR]

Significant changes in the photosynthesis of terrestrial vegetation are observed in recent decades due to climate variability, land use and land cover changes, frequent disasters and anthropogenic forcing, but this situation in the Indian context remains largely unexplored. Here, therefore, we estimate the long–term changes in photosynthesis and its key climate drivers in India. The gross photosynthetic activity peaks (FPAR mean = 0.57) in post–monsoon (September, October and November) due to high soil moisture (87.26 kg/m² ), good precipitation (5.2 mm/day) and optimum temperature (21.46 C) conditions, whereas it is smallest (FPAR mean = 0.4) in summer (March, April and May) due to very high temperature (27.37 C) and lower soil moisture (55.45 kg/m2 ). The trend in FPAR over 2000–2019 reveals enhanced photosynthetic activity in India. In the northwest, both soil moisture and temperature have a positive influence on greening (i.e. moisture induced greening). Conversely, the unfavourable soil moisture and temperature conditions dominate in the peninsular region to trigger browning there (i.e. warming induced moisture stress). Our analyses suggest that the photosynthetic trend is primarily controlled by trend in the soil moisture (44.31%) and air temperature (32.54%). The warming induced moisture stress can be a serious threat in the future to drive browning in more areas. Extreme events such as droughts, landslides and fires are expected to increase in the hilly regions such as the northeast due to browning. Furthermore, as food production and forest resources influence the economy of a country like India, management and conservation of greenery are inevitable for a sustainable ecosystem. [ABSTRACT FROM AUTHOR]

A landslip is derived from nature and sometimes caused by human activities that endanger humanity and habitation. This work focuses on landslide behaviour for sustainable landslide mitigation and susceptibility analysis. The current research aims to evaluate three state-of-the-art machine learning techniques including, Random Forest (RF), Backpropagation Neural Network (BPNN), and Bayesian Network (BN), to predict the landslide susceptibility for the Uttarakashi district, Uttarakhand (India). For this purpose, a total of 554 landslide locations and eleven influencing factors were integrated to construct a landslide susceptibility map. The landslide inventory data were separated into training and testing sets. The receiver operating characteristic (ROC) with other statistical metrics, including sensitivity, specificity, precision, recall, and accuracy, was applied to compare the performance of machine learning techniques. The results indicated that the area under the curve (AUC) value for the RF model (AUC=0.89) is high. Furthermore, our findings showed that the RF model performs the best among all the other models with the highest training and testing accuracy, 96% and 86%, respectively. The final landslide susceptibility map is grouped into three classes, i.e. Low, Moderate, and High. The study outcome would provide support to disaster management officials in effective decision-making to prioritise necessary actions. [ABSTRACT FROM AUTHOR]

Landslide susceptibility is a dynamic phenomenon; it changes with slope intrinsic and transient factors. The study aims to evaluate the changes in the landslides, their size, location, and up to some extent, gradual stabilization impacts on susceptibility assessment. The majority of the models, which are developed to analyze landslide susceptibility, use landslide inventory as a dependent variable to compute the predictors' significance. Therefore, a slight change in the landslide dataset nullifies the susceptibility zones. Thus, the landslide susceptibility maps become irrelevant to mitigate slope failures. In the present study, we have used two reference period landslide dataset to execute the landslide susceptibility analysis using the frequency ratio method. The model result was validated with a landslide test dataset using the area under the curve (AUC) method. The AUC value is 0.89 and 0.91 for 2013 and 2019, respectively. The study finds that annual updating of landslide inventory is essential for susceptibility modeling for assessment of associated risks and implications of mitigation measures along the highway corridor. [ABSTRACT FROM AUTHOR]

The study was undertaken to produce the landslide susceptibility maps by using Dempster–Shafer, Bayesian probability and logistic regression methods for the southern Western Ghats, Kerala, India. A landslide inventory database of 82 landslides is prepared and used for landslide susceptibility modelling. Twelve landslide conditioning factors including lithology, geomorphological features, slope angle, soil texture, distance from stream, distance from road, distance from lineaments, land use/land cover, slope curvature, rainfall, topographic wetness index and relative relief are extracted from the spatial database and used for modelling. Multi-collinearity among the independent variables were tested and landslide susceptibility maps are constructed. The constructed models were validated with sensitivity, specificity, classification accuracy, ROC-AUC, root mean square error (RMSE) and kappa index. The Bayesian probability model obtained highest ROC-AUC (0.833), sensitivity (0.870), specificity (0.800) and kappa index (0.667) with least RMSE (0.4550) in validation phase. In addition, the study reveals that the agricultural areas have 10°–40° slopes falling on the denudational structural hills are extremely susceptible to landslide occurrence with extended influence from distance from roads, distance from streams and soil texture. The predicted model is trustworthy for future land use planning in the southern Western Ghats to mitigate the risk from landslide hazard. [ABSTRACT FROM AUTHOR]

A robust geodetic framework comprising Terrestrial Laser Scanner (TLS), Global Navigation Satellite Systems (GNSS), Robotic Total Station (RTS) and Multi-temporal InSAR (MT-InSAR) was employed first in India to investigate a landslide-prone Sirobagarh region, Uttarakhand, at different spatial extents, and to evaluate the relationship amongst the displacement estimates obtained from the applied surveying techniques. TLS derived digital elevation models indicated displacements >5 m on the landslide upper scarp. GNSS- and RTS-based observations showed horizontal movements towards the Alaknanda river in the landslide slope direction (maximum values: 0.1305 and 0.045 m, respectively), and downward vertical motion (largest subsidence magnitude: −2.1315 and −0.030 m, respectively). MT-InSAR processing of Sentinel-1a images identified 21071 measurement pixels, highlighting subsidence around the landslide (mean velocity range: −0.110 to 0.008 m/year). Analysis of displacement vectors using vector equality, cross-covariance, cross-correlation and principal component analysis reveals that GNSS vertical displacement estimates were partially correlated with MT-InSAR measurements (correlated for epoch difference 2–3), whereas there was good cross-correlation between MT-InSAR and LiDAR observations throughout. The displacement estimates and their analyses evident unstable movement of the landslide scarp occurring due to debris flow and rainfall, and a relatively moderate subsidence activity in the surrounding areas lying in the landslide zone. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate and compare the capabilities of four machine learning methods namely LogitBoost Ensemble (LBE), Fisher's Linear Discriminate Analysis (FLDA), Logistic Regression (LR) and Support Vector Machines (SVM) to select the best method for landslide susceptibility mapping. A part of landslide prone area of Tehri Garhwal district of Uttarakhand state, India, was selected as a case study. Validation of models was carried out using statistical analysis, the chi square test and the Receiver Operating Characteristic (ROC) curve. Result analysis shows that the LBE has the highest prediction ability (AUC = 0.972) for landslide susceptibility mapping, followed by the SVM (0.945), the LR (0.873) and the FLDA (0.870), respectively. Therefore, the LBE is the best and a promising method in comparison to other three models for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

The floods in the parts of the north-west Himalayan region caused severe damage in the Uttarakhand state of India and in some parts of western Nepal. The severity of the floods and damage was maximum in the Kedarnath region, home of a very famous Hindu pilgrimage. It caused the death of about 4000 people and almost a similar number are still missing. The flash floods were mainly caused by the heavy rainfall, triggering landslides in some places, damaging buildings and infrastructures. The extensive damage and large death toll expose the vulnerability of the mountainous region and lack of coordinated relief and rescue operation. [ABSTRACT FROM PUBLISHER]

As suggested by field records, elaborate mitigative planning for the protection of an area around Amparav in North India from the threat of landslide have been in place for nearly a century. The landslide management plan incorporates essentials of both structural and non‐structural mitigative measures that reflect a thorough understanding by the landslide managers of the mass wastage processes involved. Implementation of this plan safeguarded this highly fragile zone that is neotectonically active and that has historically been threatened by stream erosion. However, critical lack of awareness of the plan among ordinary villagers led to its being rendered inoperable; culminating in the Amparav tragedy of the 23 September 2004 that took three human lives and destroyed huge amounts of public and private property and infrastructure facilities. [ABSTRACT FROM AUTHOR]