Slope stability analysis of Balia Nala landslide, Kumaun Lesser Himalaya, Nainital, Uttarakhand, India

Kumar, Mohit; Rana, Shruti; Pant, P. D.; Patel, Ramesh C.

doi:10.1016/j.jrmge.2016.05.009

Cited by 59 publications

(5 citation statements)

References 7 publications

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“…Construction, weight on the riverbank, vegetation, tectonic activity, water saturation, and other conditioning factors can all contribute to river cut failure. As a result of these conditioning factors, stress develops along riverbanks, weakening the soil strength and increasing the risk of landslides 104 .…”

Section: Methodsmentioning

confidence: 99%

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Kainthura

Sharma

2022

Sci Rep

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Natural disasters always have a damaging effect on our way of life. Landslides cause serious damage to both human and natural resources around the world. In this paper, the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared. In this approach, the Rough Set theory coupled with five different models namely Bayesian Network (HBNRS), Backpropagation Neural Network (HBPNNRS), Bagging (HBRS), XGBoost (HXGBRS), and Random Forest (HRFRS) were taken into account. The database for the models development was prepared using fifteen conditioning factors that had 373 landslide and 181 non-landslide locations that were then randomly divided into training and testing locations with a ratio of 75%:25%. The appropriateness and predictability of these conditioning factors were assessed using the multi-collinearity test and the least absolute shrinkage and selection operator approach. The accuracy, sensitivity, specificity, precision, and F-Measures, and the area under the curve (AUC)-receiver operating characteristics curve, were used to evaluate and compare the performance of the individual and hybrid created models. The findings indicate that the constructed hybrid model HXGBRS (AUC = 0.937, Precision = 0.946, F1-score = 0.926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS). Importantly, when the fusion is performed with the rough set method, the prediction capability of each model is improved. Simultaneously, the HXGBRS model proposed shows superior stability and can effectively avoid overfitting. After the core modules were developed, the user-friendly platform was designed as an integrated GIS environment using dynamic maps for effective landslide prediction in large prone areas. Users can predict the probability of landslide occurrence for selected region by changing the values of a conditioning factors. The created approach could be beneficial for predicting the impact of landslides on slopes and tracking landslides along national routes.

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Section: Methodsmentioning

confidence: 99%

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Kainthura

Sharma

2022

Sci Rep

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“…Neotectonic activities along Baliyanala Fault caused subsidence of ground is common at GIC ground at Harinagar in Nainital (Kumar et al, 2017). On the other hand, the Narayanbagar subsidence zone located on the left bank of the Pinder river is lying in proximity to the boundary of Almora and Berinag Nappe (Gupta et.…”

Section: Influence Of Structural Discontinuitiesmentioning

confidence: 99%

Increasing Risk of Silent Disaster in Uttarakhand Himalaya: An Example from Higher Himalaya

KHANDURİ,

SAKLANİ,

CHETRY

2023

Afet Ve Risk Dergisi

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Land subsidence is an abrupt and silent disaster worldwide caused by various factors such as tectonics, nature of rocks and climatic variability etc. It is directly triggered by anthropogenic activities. The Uttarakhand Himalayan province in India has witnessed several earthquakes, landslides, avalanches, droughts, and flash floods/floods in the recent past. Among these incidences landslides frequently occurs in the Himalayan region, particularly during the monsoon season. Seismically, this region falls in seismic zones V and IV due to which area witnesses frequent earthquakes. An attempt was made for preparation of an inventory of land subsidence across the state of Uttarakhand to determine the key factors that are responsible for land subsidence. Previous studies and field evidences suggests that land subsidence events mainly occur due to several factors such as carbonate rocks, topography, tectonics, seismicity, climate, flash floods/floods and reservoir drawdown effect in this region. Furthermore, anthropogenic activities such as mining, underground water, unscientifically civil constructions, inadequate drainage, heavy load on ground/slope, and modification of slope for infrastructure developments aggravates the problem. This study also highlights the problem of ongoing chronic land subsidence in Joshimath town which is situated over an old landslide mass as well as its proximity to Vaikrita Thrust.

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“…Then, it was followed by two heavy rainstorms, which increased the probability of mudslides and floods. Meanwhile, landslides blocked the rivers to form barrier lakes, part of the landslide dam collapsed, and the flood caused serious damage downstream (Xu et al, 2009;Kumar et al, 2017;Singh, 2020). Therefore, it is very important to assess the hazard of earthquake disaster chain for comprehensive disaster risk management.…”

Section: Introductionmentioning

confidence: 99%

Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning—A Case Study of Mao County, Sichuan Province

Rong

et al. 2022

Front. Earth Sci.

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Chain disasters often cause greater casualties and economic losses than single disasters. It plays an important role in the prevention and control to draw the susceptibility map and hazard map of geological hazards. To the best of our knowledge, the existing models are not suitable for the study of earthquake–geological disaster chains. Therefore, this study aims to establish a DNN model suitable for the study of earthquake–geological disaster chains. Firstly, nine key factors affecting geological disasters were selected and multi-source data sets were established based on geological disaster points in the study area. Secondly, the DNN model is trained to calculate the susceptibility of landslides and is discussed with the Support Vector Machine (SVM) model, Logistic Regression (LR) model, and Random Forest (RF) model. Finally, verify with the ROC curve. The verification results show that the DNN model has the highest accuracy among the proposed models. It is suitable for drawing geological hazard susceptibility maps and hazard maps. Therefore, it is proved that the model can be applied for the prediction of chain disasters and is a promising tool for geological hazard assessment.

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Slope stability analysis of Balia Nala landslide, Kumaun Lesser Himalaya, Nainital, Uttarakhand, India

Cited by 59 publications

References 7 publications

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Increasing Risk of Silent Disaster in Uttarakhand Himalaya: An Example from Higher Himalaya

Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning—A Case Study of Mao County, Sichuan Province

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