Browsing by Author "Harish Bahuguna"

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A glaciotectonic landform in the Shyok valley, Trans-Himalayan Karakoram Range, India
(Cambridge University Press, 2024) Abdul Qayoom Paul; Harish Bahuguna; Parveen Kumar
This study reports and discusses the first case of glaciotectonic landforms in the Shyok valley of the Trans-Himalayan Karakoram Range, Ladakh, where a large decomposed granite megablock (8.2 km2) along with underlying diamicton is thrust over the unconsolidated Quaternary glaciofluvial sediments along a fault gouge zone near the village of Khalsar.The absence of deformation signatures below the fault gouge indicates that the brittle fault acted as a décollement surface under frozen conditions along which the glaciotectonic megablock was translated.The other deformation features include slickensides, ductile shear, thrust propagation fold noses, clastic dykes and rafts of granite and slate within the diamicton sediments.These features indicate a subglacial glaciotectonic nappe origin of the landform.The presence of juxtaposed brittle to ductile deformation fabric, clastic dykes and the superimposition of deformed decomposed granite and diamicton over the undisturbed fluvial sediments indicates a permafrost glacial margin and proglacial environment under sufficient subglacial hydrodynamic conditions for the entrapment and transportation of the glaciotectonic megablock.The deformation fabric consistently shows a southeast orientation, indicating an advancing glacier motion from northwest to southeast.The Siachen Glacier which formerly flowed down the Nubra valley is the most likely cause of the Khalsar glaciotectonic landform. © The Author(s), 2024.
Advanced Bivariate Geostatistical Modeling for High-Resolution Landslide Susceptibility Zonation for Effective Risk Management in the Northwestern Himalaya, India
(Springer Science and Business Media Deutschland GmbH, 2025) Imran Khan; Vikas Yadav; Ashutosh Kainthola; Harish Bahuguna; Debi Prasanna Kanungo; Ranjan Kumar Dahal; Shantanu Sarkar; Md Sarfaraz Asgher
Frequent landslides in the northwestern Himalaya India (NHI) region cause significant loss of life and property, making landslide susceptibility zonation (LSZ) crucial for identifying vulnerable areas. This study aims to develop LSZ maps for the NHI using four bivariate geostatistical models: Frequency Ratio (FR), Weight of Evidence (WoE), Information Value (IV), and Yule's Coefficient (Yc). A total of 38,697 landslides, covering 149.50 km2, were analyzed. The data was split into 70% for training and 30% for testing, ensuring robust model validation. Twelve causative factors were considered, including slope angle, slope aspect, slope curvature, relative relief, terrain roughness index, geomorphon, distance to drainage, land use land cover, lithology, distance to fault/thrust, earthquakes, and rainfall. The models identified high to very high susceptibility zones, covering 28.7%, 32.8%, 48.1%, and 48.2% of the region for the Yc, FR, WoE, and IV models, respectively. ROC analysis revealed that the FR model achieved the highest accuracy, with 0.845 (84.5%) for both validation and prediction. The IV model followed with ROC values of 0.833 (83.3%), while the Yc model performed similarly, with values of 0.831 (83.1%). The WoE model exhibited slightly lower accuracy, with ROC values of 0.830 (83.0%) for validation and 0.831 (83.1%) for prediction. Both the WoE and IV models covered over 98% of landslide areas in high and very high susceptibility zones, indicating a tendency to overestimate highly susceptible areas. The study suggests that the FR and Yc models are particularly effective for LSZ and risk assessment. These results provide valuable insights for hazard management, aiding researchers, planners, and policymakers in selecting appropriate models for LSZ and mitigating landslide risks in other vulnerable regions. © King Abdulaziz University and Springer Nature Switzerland AG 2024.
Advanced Bivariate Geostatistical Modeling for High-Resolution Landslide Susceptibility Zonation for Effective Risk Management in the Northwestern Himalaya, India
(Springer Science and Business Media Deutschland GmbH, 2024) Imran Khan; Vikas Yadav; Ashutosh Kainthola; Harish Bahuguna; D.P. Kanungo; Ranjan Kumar Dahal; Shantanu Sarkar; Md. Sarfaraz Asgher
Frequent landslides in the northwestern Himalaya India (NHI) region cause significant loss of life and property, making landslide susceptibility zonation (LSZ) crucial for identifying vulnerable areas. This study aims to develop LSZ maps for the NHI using four bivariate geostatistical models: Frequency Ratio (FR), Weight of Evidence (WoE), Information Value (IV), and Yule's Coefficient (Yc). A total of 38,697 landslides, covering 149.50 km2, were analyzed. The data was split into 70% for training and 30% for testing, ensuring robust model validation. Twelve causative factors were considered, including slope angle, slope aspect, slope curvature, relative relief, terrain roughness index, geomorphon, distance to drainage, land use land cover, lithology, distance to fault/thrust, earthquakes, and rainfall. The models identified high to very high susceptibility zones, covering 28.7%, 32.8%, 48.1%, and 48.2% of the region for the Yc, FR, WoE, and IV models, respectively. ROC analysis revealed that the FR model achieved the highest accuracy, with 0.845 (84.5%) for both validation and prediction. The IV model followed with ROC values of 0.833 (83.3%), while the Yc model performed similarly, with values of 0.831 (83.1%). The WoE model exhibited slightly lower accuracy, with ROC values of 0.830 (83.0%) for validation and 0.831 (83.1%) for prediction. Both the WoE and IV models covered over 98% of landslide areas in high and very high susceptibility zones, indicating a tendency to overestimate highly susceptible areas. The study suggests that the FR and Yc models are particularly effective for LSZ and risk assessment. These results provide valuable insights for hazard management, aiding researchers, planners, and policymakers in selecting appropriate models for LSZ and mitigating landslide risks in other vulnerable regions. © King Abdulaziz University and Springer Nature Switzerland AG 2024.
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.
GIS-Based Multi-temporal Analysis of Landslide Susceptibility Mapping Along the Ramban-Banihal Road Section of National Highway-44, Jammu and Kashmir
(Springer Nature, 2024) Imran Khan; Harish Bahuguna; Ashutosh Kainthola; Vishnu Himanshu Ratnam Pandey
One of the most landslide-prone areas in the Jammu and Kashmir Himalaya is the Ramban-Banihal road section of National Highway - 44, which is characterized by frequent occurrences of landslides. These events cause damage to infrastructure and fatalities. To manage this landslide hazard, it is important to prepare landslide susceptibility map (LSM) by taking into account the significant causative factors in the region. In this study, a frequency ratio model was applied to assess the impact of causative factors to landslides and to prepare a landslide susceptibility map of the study area on the Geographic Information System. A total of 81 and 106 landslide events were identified from the Google Earth Image for the years 2017 and 2020 respectively, among which 70% of the landslide incidences were utilized for training the model, while the remaining 30% were utilized for testing. Thirteen factors, including slope, aspect, curvature, lithology, geomorphic, LULC, distance from fault, distance from streams, distance to road, relative relief, stream power index, topographic wetness index, terrain ruggedness index was analyzed and integrated with landslide occurrences. These factors were weighted based on the presence of landslides in their respective class and integrated in maps for the year 2017, and for the year 2019 were generated using a GIS platform. The obtained maps were categorized into three different landslide susceptibility classes, i.e., low, moderate and high. The results for the years 2017 and 2019–20 demonstrate a decrease in the region's landslide susceptibility i.e., high zone from 22.39% to 14.19% and moderate zone from 44.95% to 38.34% while an increase in the low zone landslide susceptibility from 32.67% to 47.48%. This indicates a decrease in the proportion of high and moderate susceptible zones over the previous two years because of majority impact by the anthropogenic activities. Area Under Curve (AUC) of the Receiver Operating Characteristic curve (ROC) have been used to assess the accuracy of the maps and have revealed an excellent accuracy of 0.918 and 0.942 of maps for the years of 2017 and 2019–2020 using a frequency ratio technique. These LSM’s can be used to by communities, engineers, and land-use authorities about different landslide susceptibility zones and also for future land use planning in order to reduce the damage caused by landslides. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Integrating FR, MFR and IV Models for Landslide Susceptibility Zonation Mapping in Joshimath Watershed, Uttarakhand, India
(Springer Science and Business Media Deutschland GmbH, 2025) Imran Khan; Ashutosh Kainthola; Harish Bahuguna; Vishnu Himanshu Ratnam Pandey; Md Sarfaraz Asgher; Ashish Bhardwaj; Deepali Gupta
Joshimath watershed in Uttarakhand, India, is known for experiencing creep, subsidence, and frequent minor and major landslides. Identifying zones prone to landslides through landslide susceptibility zonation (LSZ) is crucial for city planners to mitigate risks and reduce potential losses. This study employs three widely used and accurate statistical models: Frequency Ratio (FR), Modified Frequency Ratio (MFR), and Information Value (IV) to assess LSZ. A dataset of 271 landslides, derived from time-series satellite images, was utilized, with 70% (190 events) allocated for model training and 30% for validation. The analysis considered fifteen factors influencing landslide susceptibility, including slope, aspect, curvature, proximity to drainage, proximity to faults, proximity to roads, geomorphon, landform, altitude, lithology, and LULC data from both Google Earth and ESRI, RR, SPI, and TWI were evaluated, offering a comprehensive view of the various factors that may affect landslide occurrence. Based on ranking, the most influential factors are geomorphon, proximity to faults and drainage, proximity to roads, and aspect. In contrast, LULC (ESRI), RR, altitude, lithology, and slope demonstrate limited influence, while TWI, SPI, and curvature are the least influential factors. The susceptibility maps were classified into three categories. The FR model identified 59.5% of the area as low susceptibility, 32.1% as medium, and 8.3% as high, with 65.9% of landslides occurring in high-susceptibility zones. The MFR model classified 48.3% of the area as low susceptibility, 27.1% as medium, and 24.6% as high, with 78.4% of landslides located in high-susceptibility zones. The IV model classified 37.8% of the area as low susceptibility, 41.1% as medium, and 21.2% as high, with 77.4% of landslides occurring in high-susceptibility zones. ROC analysis validated the models’ predictive capabilities, with the FR model achieving the highest accuracy in both the Landslide Susceptibility Index (LSI) and LSZ at 83.1% and 84.5% AUC, respectively. The MFR and IV models also demonstrated commendable performance, providing valuable insights for landslide risk assessment. The findings emphasize the importance of model selection in LSZ mapping, highlighting the FR and MFR models as effective tools for risk management and land-use planning in landslide-prone areas. This study contributes to landslide susceptibility modeling and provides a framework for future research in geological hazard assessment. © The Author(s), under exclusive licence to Shiraz University 2024.
Integrating FR, MFR and IV Models for Landslide Susceptibility Zonation Mapping in Joshimath Watershed, Uttarakhand, India
(Springer Science and Business Media Deutschland GmbH, 2024) Imran Khan; Ashutosh Kainthola; Harish Bahuguna; Vishnu Himanshu Ratnam Pandey; Md. Sarfaraz Asgher; Ashish Bhardwaj; Deepali Gupta
Joshimath watershed in Uttarakhand, India, is known for experiencing creep, subsidence, and frequent minor and major landslides. Identifying zones prone to landslides through landslide susceptibility zonation (LSZ) is crucial for city planners to mitigate risks and reduce potential losses. This study employs three widely used and accurate statistical models: Frequency Ratio (FR), Modified Frequency Ratio (MFR), and Information Value (IV) to assess LSZ. A dataset of 271 landslides, derived from time-series satellite images, was utilized, with 70% (190 events) allocated for model training and 30% for validation. The analysis considered fifteen factors influencing landslide susceptibility, including slope, aspect, curvature, proximity to drainage, proximity to faults, proximity to roads, geomorphon, landform, altitude, lithology, and LULC data from both Google Earth and ESRI, RR, SPI, and TWI were evaluated, offering a comprehensive view of the various factors that may affect landslide occurrence. Based on ranking, the most influential factors are geomorphon, proximity to faults and drainage, proximity to roads, and aspect. In contrast, LULC (ESRI), RR, altitude, lithology, and slope demonstrate limited influence, while TWI, SPI, and curvature are the least influential factors. The susceptibility maps were classified into three categories. The FR model identified 59.5% of the area as low susceptibility, 32.1% as medium, and 8.3% as high, with 65.9% of landslides occurring in high-susceptibility zones. The MFR model classified 48.3% of the area as low susceptibility, 27.1% as medium, and 24.6% as high, with 78.4% of landslides located in high-susceptibility zones. The IV model classified 37.8% of the area as low susceptibility, 41.1% as medium, and 21.2% as high, with 77.4% of landslides occurring in high-susceptibility zones. ROC analysis validated the models’ predictive capabilities, with the FR model achieving the highest accuracy in both the Landslide Susceptibility Index (LSI) and LSZ at 83.1% and 84.5% AUC, respectively. The MFR and IV models also demonstrated commendable performance, providing valuable insights for landslide risk assessment. The findings emphasize the importance of model selection in LSZ mapping, highlighting the FR and MFR models as effective tools for risk management and land-use planning in landslide-prone areas. This study contributes to landslide susceptibility modeling and provides a framework for future research in geological hazard assessment. © The Author(s), under exclusive licence to Shiraz University 2024.
Landslide Susceptibility Analysis in the Ramban Basin, Jammu and Kashmir: A Statistical Approach for Comprehensive Susceptibility Assessment
(Springer Nature, 2024) Imran Khan; Ashutosh Kainthola; Harish Bahuguna
The Ramban district, located in the northern Himalayas inside the Union Territory of Jammu and Kashmir, is well-known for its increased vulnerability to landslides. This region consistently faces a repetitive occurrence of landslides that result in loss of life and cause significant harm to both cultivated and non-cultivated regions, essential infrastructure, and properties. Given the need to reduce these negative effects, the creation of a landslide susceptibility map is seen as a vital approach. This study systematically utilized a Geographic Information System (GIS)-based Information Value Model (IVM) to methodically gather a thorough inventory of 796 landslides. The landslide inventory was divided into training datasets (70%) for model prediction and testing datasets (30%) for reliable model validation. The analysis encompassed thirteen contributing elements to landslides, namely altitude, slope, aspect, curvature, distance to drainage, distance to structural lineaments, geomorphon, land use land cover, lithology, relative relief, stream power index, and topographic wetness index. The IVM approach was subsequently utilized to determine the weight of each element and factor class based on their correlation with training landslides. The combination of these efforts led to the creation of a map that shows the likelihood of landslides occurring. This was accomplished by combining the weights of all factors contributing to landslides using the raster calculator in GIS. The IVM's ability to predict landslide susceptibility was thoroughly evaluated by calculating the area under the curve (AUC) of the receiver operating characteristic (ROC) curves for both the training and testing datasets. The model demonstrated a notable AUC accuracy of 71.4% for the success rate and 70.5% for the prediction rate. Surprisingly, when the Landslide sensitivity Map (LSM) was superimposed, 91.3% of the landslide pixel area was categorized as having a very high or high susceptibility to landslides. The results reveal important insights, identifying places with a very high level of vulnerability. Specifically, areas with extremely high susceptibility and high susceptibility make up 13.9% and 23.5% of the total area, respectively. Areas with moderate susceptibility cover 29.1% of the entire territory, while areas with low and extremely low susceptibility together account for 33.5% of the total area. Landslide susceptibility mapping improves our comprehension of the probability of slope instability and serves as a vital tool for making well-informed decisions in land use planning and methods to reduce risk. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Regional landslide susceptibility zonation utilizing bivariate statistical techniques in the northwestern Himalayas, Jammu and Kashmir, India
(Springer, 2024) Imran Khan; Harish Bahuguna; Ashutosh Kainthola
This research focuses on assessing landslide susceptibility in the Jammu and Kashmir (J&K) region of the northwestern Himalayas, which is known for its high incidence of landslides. Utilizing advanced geographic information system (GIS) techniques, 18 influencing factors, including terrain characteristics, land use, rainfall, and lithology, were incorporated to create a comprehensive landslide susceptibility map (LSM). Leveraging a robust database comprising 6669 landslides, with 70% utilized for modelling and 30% for validation, the study utilized a Yule's coefficient (YC). The resulting LSM, categorized into five susceptibility zones, indicates that one third of the study area is highly susceptible to landslides, with 9.9, 23.9, 27.9, 23.1, and 15.2% falling into very high, high, moderate, low, and very low susceptibility zones, respectively. The model’s accuracy was validated with an 80.9% success rate through receiver operating curve (ROC) analysis. This LSM serves as a crucial tool for regional planning and management, providing valuable insights to mitigate landslide hazards. It facilitates informed decision-making and proactive measures and enhances resilience in landslide-prone areas, thereby contributing to the sustainable development and safety of the J&K Himalayan region. © Indian Academy of Sciences 2024.
Unravelling the impact of landslide inventory on landslide susceptibility in the Indian Himalaya
(Elsevier Ltd, 2025) Imran Khan; Ashutosh Kainthola; Harish Bahuguna; Rayees Ahmed; Mohamed Abioui
Landslide susceptibility zonation (LSZ) mapping is heavily influenced by the raster resolution and landslide inventory types. The effect of landslide inventories (polygon and point) at three raster resolutions (12.5 m, 30 m, and 90 m) on LSZ analysis is investigated in this work. The Ramban District sub-basin in Jammu and Kashmir, identified as the most vulnerable area, encompasses 302 landslides. To ensure a robust susceptibility assessment, Yule's coefficient (Yc) was utilized to examine twelve landslide conditioning factors (LCFs) for LSZ preparation. LULC (ESRI & Google) and road variables have the greatest influence at all resolutions, but lithology plays a critical role in lower-resolution polygon-based data. Aspect, geomorphology, slope, and landform exhibit moderate to low effects, which vary with resolution. LULC, roads, and lithology emerge as key influences, whereas drainage, faults, and landforms serve as secondary influences. RR and TWI demonstrate negligible influence on LSZ across all sampling and resolution. LSZ exhibits considerable variation with resolution in point-based inventory. At higher resolutions (12.5 m and 30 m), raster area coverage is below 50 % of vector coverage. Conversely, at 90 m, raster coverage roughly doubles that of vector data, potentially inflating LSZ results. AUC values are higher for point than for polygon sampling. However, for precise mapping, polygon sampling gives a more accurate picture of factors and landslide distribution. This study emphasizes the significance of using polygon sampling to delineate landslide susceptibility in the Himalayas. © 2025 Elsevier Ltd