Praveen Kumar

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

PhD
Deep Neural Networks for Predicting the Soil Movements in the Real-world Landslides

About

17
Publications
6,186
Reads
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37
Citations
Introduction
Slope movement prediction via machine-learning algorithms.
Additional affiliations
November 2017 - present
Indian Institute of Technology Mandi
Position
  • MS
Description
  • Slope movement prediction via machine-learning algorithms.
Education
August 2010 - June 2013
S.S. Jain Subodh P.G. College Jaipur
Field of study
  • Computer Application

Publications

Publications (17)
Chapter
Full-text available
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...
Conference Paper
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Thesis
Full-text available
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Conference Paper
Full-text available
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...
Chapter
Full-text available
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...
Article
Full-text available
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...
Chapter
Full-text available
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...

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