Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Abstract Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos...

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Autores principales:	Sunil Saha, Raju Sarkar, Jagabandhu Roy, Tusar Kanti Hembram, Saroj Acharya, Gautam Thapa, Dowchu Drukpa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/88429031e1d44212a13b75c1621965c5
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spelling	oai:doaj.org-article:88429031e1d44212a13b75c1621965c52021-12-02T16:28:06ZMeasuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms10.1038/s41598-021-95978-52045-2322https://doaj.org/article/88429031e1d44212a13b75c1621965c52021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95978-5https://doaj.org/toc/2045-2322Abstract Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may—support landslide prediction and management.Sunil SahaRaju SarkarJagabandhu RoyTusar Kanti HembramSaroj AcharyaGautam ThapaDowchu DrukpaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-23 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sunil Saha Raju Sarkar Jagabandhu Roy Tusar Kanti Hembram Saroj Acharya Gautam Thapa Dowchu Drukpa Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms
description	Abstract Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may—support landslide prediction and management.
format	article
author	Sunil Saha Raju Sarkar Jagabandhu Roy Tusar Kanti Hembram Saroj Acharya Gautam Thapa Dowchu Drukpa
author_facet	Sunil Saha Raju Sarkar Jagabandhu Roy Tusar Kanti Hembram Saroj Acharya Gautam Thapa Dowchu Drukpa
author_sort	Sunil Saha
title	Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms
title_short	Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms
title_full	Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms
title_fullStr	Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms
title_full_unstemmed	Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms
title_sort	measuring landslide vulnerability status of chukha, bhutan using deep learning algorithms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/88429031e1d44212a13b75c1621965c5
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Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

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