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The 270 km long National Highway (NH-44) is the only way that connects the Kashmir valley to the rest of India. Stretches of the expressway go through extremely dubious terrains and mountains. Consequently, blockage of NH-44 has turned into a repetitive wonder for past numerous years which affects the state economy and trade. The NH-44 national hig...
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... The prominent landslidesusceptible states include Uttarakhand, Jammu and Kashmir, and Himachal Pradesh in northern India and Assam, Manipur, Meghalaya, Sikkim, and Manipur in northeast India (NDMA 2019). Ramsoo, Nashri, Maroog, Panthyal, Kheri in Ramban, and many areas of Udhampur district fall in the landslide active zones along NH-44, Jammu and Kashmir (Bhat et al. 2021;Hussain et al. 2019;Fayaz and Khader 2020). In India, NH-44, which connects Srinagar in the north, with Kanyakumari in the south is the country's longest national highway. ...
Landslides along the motorways in the Himalayan region cause huge damage to life and the economy. Such events are frequent and render the landlocked regions disconnected from the rest of the world for days together. Identifying the hotspot locations for landslide occurrences becomes crucial to reduce the damage caused by landslides. The present study was carried out to analyse five finite slopes under normal or no rainfall and high-intensity rainfall conditions to understand the impact of rainfall on the stability of slopes along National Highway-44 in the Kashmir Himalayas. Geotechnical characterization of the soil samples, both disturbed representative and undisturbed, collected from the selected locations was performed. Rainfall data for the past 10 years was analysed and used for numerical analysis of excess water pore pressure generation and slope stability. The initial condition of excess pore water pressure within the slopes was developed using annual average rainfall, whereas for transient seepage analysis, maximum daily rainfall for 1 day was used. Limit equilibrium-based Morgenstern and Price method used for the determination of factor of safety of slopes. The results showed that all the slopes are stable under normal conditions except the Ramsoo slope. However, under the transient conditions with a maximum daily rainfall scenario, the stability of the slopes is reduced. A significant decrease of 36% in factor of safety (FOS) was observed due to the reduction of matric suction within the slopes. The rate of reduction in FOS was controlled by slope geometry (steepness) and slope material (clay fraction). The results reveal a strong need for detailed investigation, interventions, and landslide mitigation measures for the studied stretch of the all-important NH-44 in the Kashmir Himalayas.
... (accessed on 1 December 2021) and from local sources (newspapers, social media, and online news reports). Some soil characteristics and threshold values were derived from Fayaz and Khader (2020) [41]. The same threshold values were used to predict landslides using machine learning methods (algorithm). ...
... In this paper, a landslide prediction model was designed using various ma learning algorithms. The algorithms used were Multiple Linear Regression, Ad Neuro-Fuzzy Inference System (ANFIS), Random Forest, and Decision Tree; the m accuracies were compared to determine the optimal prediction system for landslide application of these models in landslide engineering has been discussed in detail by and Khader (2020) [41]. The overall methodology used in the present study is sho Figure 5. ...
... The algorithms used were Multiple Linear Regression, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest, and Decision Tree; the model accuracies were compared to determine the optimal prediction system for landslides. The application of these models in landslide engineering has been discussed in detail by Fayaz and Khader (2020) [41]. The overall methodology used in the present study is shown in Figure 5. ...
Landslides are critical natural disasters characterized by a downward movement of the land masses. As one of the deadliest disasters worldwide, it takes a heavy death toll every year and creates terrible economic damage. The transition between the urban and rural areas is characterized by Highways, which in rugged Himalayan terrains have to be constructed by cutting the mountains, thereby destabilizing them and making them prone to landslides. This study has been conducted in one of the most landslide-prone regions of the entire Himalayan belt, i.e., National Highway NH-44 (Jammu-Srinagar stretch). The main objectives of this study are to understand the causes behind the heavy recurrence of the landslides in this region and propose a landslide early warning system (LEWS) based on the most suitable machine learning algorithms among the four selected, i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and decision tree. It was found that ANFIS and random forest outperformed the other proposed methods with a substantial increase in overall accuracy. The LEWS model was developed using the land system parameters that govern the landslide occurrence, such as the rainfall, soil moisture, distance to road and river, slope, land surface temperature (LST), and the built-up area near the landslide site (BUA). The developed LEWS was validated using various statistical error assessment tools such as root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error estimation, and area under the receiver operating characteristics (ROC-AUC curve). The outcomes of this study shall help manage landslide hazards in the Himalayan Urban-Rural transition zones and serve sample study in similar mountainous regions of the world.
The Jammu-Srinagar National Highway is the critical road connection between Kashmir valley and the rest of India. It passes through extremely steep slopes and high mountains prone to mass movements, particularly landslides and rockslides. Most mountainous roads are constructed on fragile and rocky slopes, and any natural (e.g., precipitation) or human-triggered disturbance (e.g., heavy traffic) can cause a fatal and devastating landslide under the influence of gravity. Many landslide-prone sites along the Highway are responsible for the continuous blockade almost throughout the year but peaking particularly during winters. As a result, it has a high toll on the state's economy and is responsible for many human casualties yearly. The present study aims to characterize various factors and their threshold values responsible for triggering a landslide. Through extensive field surveys, we evaluated different geotechnical parameters of soils at the most landslide-prone site along the Highway and augmented it with the satellite remote sensing datasets to determine the threshold values that trigger a landslide and assess the probability of occurrence of landslide events in the future using Autoregressive Moving Average (ARIMA) model and IBM SPSS Forecasting Model. This work shall help devise countermeasures for managing the landslides in the study area locally and shall serve as the guiding framework for using artificial intelligence and machine learning techniques for hazard management in the Himalayas.
A landslide is a natural phenomenon influenced by gravity which causes downward movement of materials such as soil, rocks, mud, and so on. In the past, landslides have triggered numerous mishaps and are a key danger to human life and assets. Due to the collision of Indian and Eurasian plates, Himalayan mountain range was formed after a huge bang. Continuous moment of plates makes the landscape/slope brittle, fragile, and vulnerable to landslides. Risk preparedness and an effective alert system are necessary to avoid the loss of human life and property. There are natural as well as anthropogenic factors that trigger landslides on the steep slopes. Various data analysis operations, remote sensing techniques, and field sampling experiments (Direct share, natural density, Atterberg Limits (plastic limit, liquid limit, plasticity index) Moisture content, and Specific gravity) are performed to find out the leading cause of land failures to mitigate its effects on the local population. This chapter focusses on the causes of the occurrence of the landslides on the national highway NH-44 and concludes with the mitigation and management measures to be undertaken in order to reduce the damages it imbibes on the economy of the UT of Jammu and Kashmir, India.
