Staff View: Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region

Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region

Landslides are an unpredictable natural disaster, but steps can be taken to reduce their impact. The Landslide Susceptibility Index plays a critical role in minimizing the risk of living in landslide-prone areas. Effective planning and management of these locations are essential. In recent years, st...

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Main Authors:	Bhattacharya S., Ali T., Chakravortti S., Pal T., Majee B.K., Mondal A., Pande C.B., Bilal M., Rahman M.T., Chakrabortty R.
Other Authors:	57219231264
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Published:	Springer Science and Business Media Deutschland GmbH 2025
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spelling	my.uniten.dspace-369522025-03-03T15:46:04Z Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region Bhattacharya S. Ali T. Chakravortti S. Pal T. Majee B.K. Mondal A. Pande C.B. Bilal M. Rahman M.T. Chakrabortty R. 57219231264 57203070870 59452218000 59452652800 57890574900 57203804633 57193547008 56603873300 55782542300 57208780685 Landslides are an unpredictable natural disaster, but steps can be taken to reduce their impact. The Landslide Susceptibility Index plays a critical role in minimizing the risk of living in landslide-prone areas. Effective planning and management of these locations are essential. In recent years, statistical methods and, increasingly, machine learning-based approaches have gained popularity for landslide susceptibility modeling. This study employs various machine learning and deep learning algorithms, specifically Random Forest (RF), Artificial Neural Network (ANN), and Deep Learning Neural Network (DLNN), to estimate landslide susceptibility in Chamoli district, Uttarakhand, India?a region that witnessed over a thousand landslides in 2023. We carefully selected relevant metrics based on existing research and conducted a multicollinearity analysis on each parameter to ensure the model?s accuracy. We randomly split the data into training and validation sets in a 70/30 ratio. Among the models used, the DLNN outperformed others, superiorly predicting landslide susceptibility. These findings are valuable for local government efforts in disaster prevention and mitigation, particularly in the Chamoli District of Uttarakhand, where Geographical Information System (GIS)-based susceptibility mapping plays a critical role in identifying vulnerable areas. Overall, this model evaluation framework can be used as a guide to select the most suitable modelling strategy for assessing landslide susceptibility. This type of outcome is valuable to the decision-maker to implement a more optimal strategy for reducing the probability of landslides and its associated damages. ? King Abdulaziz University and Springer Nature Switzerland AG 2024. Article in press 2025-03-03T07:46:03Z 2025-03-03T07:46:03Z 2024 Article 10.1007/s41748-024-00530-w 2-s2.0-85210739940 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210739940&doi=10.1007%2fs41748-024-00530-w&partnerID=40&md5=3b85e0cda0cbea6f508b79f772946c9c https://irepository.uniten.edu.my/handle/123456789/36952 Springer Science and Business Media Deutschland GmbH Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Landslides are an unpredictable natural disaster, but steps can be taken to reduce their impact. The Landslide Susceptibility Index plays a critical role in minimizing the risk of living in landslide-prone areas. Effective planning and management of these locations are essential. In recent years, statistical methods and, increasingly, machine learning-based approaches have gained popularity for landslide susceptibility modeling. This study employs various machine learning and deep learning algorithms, specifically Random Forest (RF), Artificial Neural Network (ANN), and Deep Learning Neural Network (DLNN), to estimate landslide susceptibility in Chamoli district, Uttarakhand, India?a region that witnessed over a thousand landslides in 2023. We carefully selected relevant metrics based on existing research and conducted a multicollinearity analysis on each parameter to ensure the model?s accuracy. We randomly split the data into training and validation sets in a 70/30 ratio. Among the models used, the DLNN outperformed others, superiorly predicting landslide susceptibility. These findings are valuable for local government efforts in disaster prevention and mitigation, particularly in the Chamoli District of Uttarakhand, where Geographical Information System (GIS)-based susceptibility mapping plays a critical role in identifying vulnerable areas. Overall, this model evaluation framework can be used as a guide to select the most suitable modelling strategy for assessing landslide susceptibility. This type of outcome is valuable to the decision-maker to implement a more optimal strategy for reducing the probability of landslides and its associated damages. ? King Abdulaziz University and Springer Nature Switzerland AG 2024.
author2	57219231264
author_facet	57219231264 Bhattacharya S. Ali T. Chakravortti S. Pal T. Majee B.K. Mondal A. Pande C.B. Bilal M. Rahman M.T. Chakrabortty R.
format	Article
author	Bhattacharya S. Ali T. Chakravortti S. Pal T. Majee B.K. Mondal A. Pande C.B. Bilal M. Rahman M.T. Chakrabortty R.
spellingShingle	Bhattacharya S. Ali T. Chakravortti S. Pal T. Majee B.K. Mondal A. Pande C.B. Bilal M. Rahman M.T. Chakrabortty R. Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region
author_sort	Bhattacharya S.
title	Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region
title_short	Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region
title_full	Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region
title_fullStr	Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region
title_full_unstemmed	Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region
title_sort	application of machine learning and deep learning algorithms for landslide susceptibility assessment in landslide prone himalayan region
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2025
_version_	1825816150677651456
score	13.244413

Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region

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