Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F22%3A00125019" target="_blank" >RIV/00216224:14310/22:00125019 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/B9780323898614000385" target="_blank" >https://www.sciencedirect.com/science/article/pii/B9780323898614000385</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/B978-0-323-89861-4.00038-5" target="_blank" >10.1016/B978-0-323-89861-4.00038-5</a>
Alternative languages
Result language
angličtina
Original language name
Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning
Original language description
In the Himalayan region, landslides are considered one of the most common natural disasters. The study area for this study is a 2 km buffer along the Thimphu-Phuentsholing highway in Bhutan, which is a part of Asian Highway 48. In this study, machine learning was adopted which allows relatively precise predictions to be made by providing accurate and reliable data. Of the numerous methods available for machine learning, two methods, i.e., random forest (RF) and logistic regression (LR) have been selected for this paper. Slope, aspect, geology, land cover, precipitation, distance from the drainage, distance from the road, TPI, TRI, Elevation, and surface roughness were the parameters selected for the study area. The two methods are validated and compared using the ROC and (AUC). The RF method performed slightly better than the LR method with an AUC of 0.91 and LR of 0.86.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10500 - Earth and related environmental sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management
ISBN
9780323898614
Number of pages of the result
17
Pages from-to
601-617
Number of pages of the book
683
Publisher name
Elsevier
Place of publication
Cambridge
UT code for WoS chapter
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