Browsing by Author "Gaurav Kumar Kushwaha"

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Decoding Landslide Susceptibility in Wayanad District of Kerala, India, Using Machine Learning Approach
(Springer Science and Business Media Deutschland GmbH, 2025) Imran Khan; Ashutosh Kainthola; Harish Bahuguna; Vikas Yadav; Vishnu Himanshu Ratnam Pandey; Gaurav Kumar Kushwaha
Landslide Susceptibility Zonation (LSZ) is essential for comprehending and predicting landslide events, especially in areas prone to natural hazards. This study assesses and contrasts the efficacy of two machine learning (ML) algorithm, Random Forest (RF) and Support Vector Machine (SVM), in producing high resolution LSZ maps for the Wayanad area in Kerala, India. The region is significantly susceptible to landslides, as evident by a disastrous occurrence on July 30, 2024, which led to more than 300 deaths and impacted almost 5,000 individuals. LSZ map was created using twelve landslide conditioning factors (LCFs) at a spatial resolution of 12.5 × 12.5 m. The evaluation of multicollinearity confirmed the independence of the explanatory factors. The model training utilized a balanced dataset consisting of 314 landslide and 314 non-landslide sites. The RF model revealed high susceptibility zones including 23.8% of the study region, while the SVM model recognized 19.5%. These zones are primarily located along the southwestern, western, and northwestern boundaries of Wayanad. The predictive capacities of the models, assessed using Receiver Operating Characteristic (ROC) analysis, demonstrated accuracies of 95.8% for RF and 93.5% for SVM, reflecting the strong performance of both techniques. The findings highlight the efficacy of ML algorithm, particularly RF, in LSZ, offering critical insights for hazard mitigation and land-use planning in comparable geologically vulnerable areas. © King Abdulaziz University and Springer Nature Switzerland AG 2025.
Empirical and numerical hill slope health evaluation at Malling Nala, NH-505, Himachal Pradesh, India
(Springer, 2025) Md Alquamar Azad; Ashutosh Kainthola; Yewuhalashet Fissha; Gaurav Kumar Kushwaha; Vikas Yadav; T. N. Singh
Slope failures and rock mass movements are continuous geomorphic processes, particularly in a dynamically charged terrane like the Himalaya. Thus, failures emanating from weak geology, hydrogeology and anthropogenic disturbances are aplenty. Present research evaluates slope stability in the vicinity of Malling Nala, along NH-505 in Himachal Pradesh, India. For the two most vulnerable sections in the study area, geo-mechanical and structural attributes have initially been ascertained. Field surveys and laboratory tests identified weak and weathered mica schist and gneissic rocks in the study area. Kinematic analysis, Rock Mass Rating (RMR), Geological Strength Index (GSI), Slope Mass Rating (SMR), modified Global Slope Performance Index (mGSPI) led to determination of possible failure mechanism and rock mass behaviour. Finite element analysis provided a comprehensive understanding of slope behaviour under various conditions, highlighting significant shear strain and displacement in both sections. As noticed from the field and classification schemes, planar and localized bench failures were established. Slope section L-1 was found to collapse under saturated water condition, manifesting the influence of snow melt. The findings indicate that both natural and human factors are causing instability. Effective risk management and mitigation strategies are essential to maintain the stability and reliability of this critical frontier region. © The Author(s) 2025.
Field data driven rockfall hazard and risk assessment along Sangla-Chitkul road, Himachal Pradesh, India
(Springer Science and Business Media Deutschland GmbH, 2025) Vishnu Himanshu Ratnam Pandey; Gaurav Kumar Kushwaha; Ashutosh Kainthola; Vikas Yadav; T. N. Singh; Abhi S. Krishna
This study addresses the issue of rockfall hazard in Baspa Valley, Himachal Pradesh, India. Proven empirical rockfall hazard rating systems were interpolated in the GIS-environment to analyse the contingent risk to the population. Initially, field data were ascertained from precarious rockfall locations along the 23 km long Sangla-Chitkul road. This was followed by a kinematic analysis to identify potential structural failure modes, revealing that each studied slope section could undergo one or a combination of failures. Rockfall Hazard Rating System (RHRS) parameters were formulated and interpolated for the entire area using the inverse-distance weighting (IDW) technique. Resulting hazard map, overlaid with population data, classified rockfall risk into five categories: very high (14.7%), high (28.8%), moderate (23.2%), low (18.6%), and very low (14.7%). A similar assessment using the Missouri Rockfall Hazard Rating System (MORFH-RS) showed the following rockfall risk distribution: very high (11%), high (21%), moderate (21%), low (21%), and very low (11%). Additionally, MORFH-RS indicated that around 90% of the area lies in high-risk and high-consequence zone, with high consequences for all locations. Two-dimensional stochastic simulations were conducted to understand rockfall dynamics at all studied locations, revealing that most sites exhibit kinetic energy exceeding 500 kJ, with five locations surpassing 1000 kJ. This indicates a high potential for significant damage across a large area of the valley, based on runout-distance data. Additionally, these findings were correlated with geotechnical characterization using Global Slope Performance Index (GSPI), identifying the potential for four distinct failure types in the valley. © The Author(s) 2025.