Praveen Kumar

Indian Institute of Technology Mandi | भारतीय प्रौद्योगिकी संस्थान मंडी · Computer Science Programme

Landslides plague the Himalayan region, and landslide occurrence is widespread in hilly areas. Thus, it is important to predict soil movements and associated landslide events in advance of their occurrence. A recent approach to predicting soil movements is to use machine-learning techniques. In machine-learning literature, both moving-average-based...

Landslides and associated slope movements are common occurrences in the hilly regions. In particular, Tangni in Uttrakhand state between Pipalkoti and Joshimath has experienced a number of landslides in the recent past. Prior research has used certain moving average and machine-learning (ML) algorithms to predict slope movements. However, a compari...

Landslides and associated soil movements (debris-flow) are the common natural calamities in the hilly regions. In particular, Tangni in Uttrakhand state between Pipalkoti and Joshimath has experienced a number of landslides in the recent past. Prior research has used certain machine-learning (ML) algorithms to predict landslides. However, a compari...

Landslides are widespread disasters in hilly regions. These disasters cause lots of injuries and deaths every year. Due to these injuries and deaths, it is imperative to monitor landslides and to warn people about impending disasters timely. It is also important to predict soil movements ahead of time so that people get enough lead time to evacuate...

Landslide incidence is common in hilly areas. In particular, Tangni in Uttrakhand state between Pipalkoti and Joshimath has experienced a number of landslide incidents in the recent past. Thus, it is important to forecast slope-movements and associated landslide events in advance of their occurrence to avoid the associated risk. A recent approach t...

Landslides are a major natural hazard that can cause significant damage and loss of life. They are often triggered by heavy rainfall, earthquakes, or other factors that can destabilize the soil and rock. To mitigate risks associated with landslides, it is important to predict where and when they are likely to occur. In this study, we developed a mu...

Seismic energy forecasting is critical for hazard preparedness, but current models have limits in accurately predicting seismic energy changes. This paper fills that gap by introducing a new ensemble random forest model designed specifically for seismic energy forecasting. Building on an existing paradigm, provided by Raghukanth et al. (2017), the...

The National Institutional Ranking Framework (NIRF) was established by the Indian Ministry of Education (MoE), which ranks higher education institutions across the country. The institutions being ranked may benefit if their rankings or performance could be predicted given their parameters, thereby, improving their Institute's attributes. However, l...

The Himalayan region faces an escalating threat from landslides, a situation worsened by climate change. These events endanger human lives and valuable properties, underscoring the necessity for robust prediction and mitigation strategies. This research presents machine learning (ML) models to forecast slope movements in landslides, including Long...

The Himalayan region encompasses nearly 12% of the total geographical area of India and exhibits a high susceptibility to landslides owing to its delicate lithology, intricate geology, and steep slopes. Over the past few decades, GIS-based hydrological, data-driven, and physical-based models have been used extensively to identify landslide-prone ar...

Extreme weather events and global climate change have exacerbated the problem of evaporation rates. Thus, accurately predicting soil moisture evaporation rates affecting soil cracking becomes crucial. However, less is known about how novel feature engineering techniques and machine-learning predictions may account for estimating the soil moisture e...

This study focuses on predicting soil movement in the Himalayan region using machine learning models. It introduces a new model called Hierarchical Transformer Prediction Autoencoder (H-TPA) and compares it with other neural networks. The study also uses Variable Sensitivity Analysis to identify environmental triggers for soil movements. Results sh...

Landslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat. Accurately predicting soil movement from monitoring data is challenging due to its complexity and inherent class imbalance. This study pro...

The present invention relates to an IoT and MEMS-based low-cost subsurface landslide monitoring and early warning system comprising a sensing unit, data logging and thresholding unit, and an alerting unit. The sensing unit further consists of multiple nodes comprising of a plurality of sensors such that each sensor is connected to the master-microc...

Due to intensifying climate change impacts, landslides have become increasingly threatening in the Himalayan region, particularly in India's Kamand Valley. This study addresses the pressing need for accurate landslide prediction models by leveraging advanced Landslide Monitoring Systems (LMSs) and machine learning techniques. A significant challeng...

The Himalayan region, particularly Himachal Pradesh, faces an escalating threat from landslides intensified by climate change. These landslides imperil human lives and valuable properties, demanding effective prediction and mitigation measures. This study develops advanced machine learning (ML) models for soil movement prediction in landslide-prone...

Movement of soil and associated landslides frequently occur in hilly areas. Regular monitoring, accurate prediction, and timely alerting of people about soil movements on hills susceptible to landslides are essential due to the potential destruction to life and property. A more recent strategy for predicting soil movement is the use of machine lear...

The landslides are a challenging problem in the Himalayan states such as Himachal Pradesh and Uttarakhand in India and as well as in the world. Machine learning models could be developed to predict the movement of landslides in advance. In our proposed study, we developed a univariate, multivariate, and ensemble multilayer perceptron (MLP) and trai...

The Himalayan mountains are prone to landslide disasters, which cause injury and fatalities among people. Remote sensing, particularly interferometric synthetic aperture radar (InSAR) based analyses, may help find the surface subsidence velocities, the rate of vertical movement of the Earth's surface downward. These subsidence velocities may help i...

Landslides are a challenging problem in India and the world. Different weather conditions and soil properties could trigger landslides. The Machine learning (ML) models could predict landslides' movements. The ML model may overfit the high-dimensional feature of weather and soil data. Dimension reduction techniques could reduce the dimension of the...

Vegetation is needed to improve soil slope stability. The roots of different species stabilize the ground by their tensile strength. However, how the tensile strength is governed by different root and shoot characteristics is less known. In this study, root tensile strength was investigated, and root and shoot characteristics were simultaneously me...

Machine learning (ML) proposes an extensive range of techniques, which could be applied to forecasting soil movements using historical soil movements and other variables. For example, researchers have proposed recurrent ML techniques like the long short-term memory (LSTM) models for forecasting time series variables. However, the application of nov...

Natural disasters such as landslides cause a lot of damage to life and property. However, less is known on how one could generate accurate alerts against landslides sufficiently ahead in time. The primary objective of this research is to develop and cross-validate a new ensemble gradient boosting algorithm for generating specific alerts about impen...

Landslides are widespread disasters in hilly regions. These disasters cause lots of injuries and deaths every year. Due to these injuries and deaths, it is imperative to monitor landslides and to warn people about impending disasters timely. It is also essential to predict slope movements ahead of time so that people get enough lead time to evacuat...

The Tangni landslide in Chamoli, India, has experienced several landslide incidents in the recent past. Due to the fatalities and injuries caused, it is essential to predict slope movements at this site. A recent approach to predicting slope movements is via machine-learning algorithms. In machine learning literature, recurrent neural networks (sim...

Landslides are a major societal threat, causing adverse consequences to life, economy and environment. Mitigation of the potential negative effects of landslides commonly involves deployment of challenging and costly measures. This is often the case in the development and operation of linear infrastructures such as road, pipeline and railway networ...

The problem of soil movements and associated landslides is common in the areas of Himachal Pradesh state in India due to the hilly terrain. Prediction of soil movements ahead of time may help save lives and infrastructure. Prior research has used machine learning models to predict soil movements but a comparison of different models for soil movemen...

Landslides are a major societal threat, causing adverse consequences to life, economy and environment. Mitigation of the potential negative effects of landslides commonly involves deployment of challenging and costly measures. This is often the case in the development and operation of linear in-frastructures such as road, pipeline, and railway netw...

Air-quality is degrading in developing countries and there is an urgent need to monitor and predict air-quality online in real-time. Although offline air-quality monitoring using hand-held devices is common, online air-quality monitoring is still expensive and uncommon, especially in developing countries. The primary objective of this paper is to p...

Air-quality is degrading in developing countries and there is an urgent need to monitor and predict air-quality online in real-time. Although offline air-quality monitoring using hand-held devices is common, online air-quality monitoring is still expensive and uncommon, especially in developing countries. The primary objective of this paper is to p...

This is a video that appeared on BBC Click covering the low-cost landslide monitoring and warning system.

Among different natural disasters, landslides are widespread in hilly areas. For landslide monitoring, one needs to collect data via weather stations about the prevailing weather and soil properties at remote locations that are prone to landslides. Due to the non-availability of grid-power, one may need to depend upon large-sized solar panels and b...