Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10500 - Earth and related environmental sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management

ISBN

9780323898614

Počet stran výsledku

17

Strana od-do

601-617

Počet stran knihy

683

Název nakladatele

Elsevier

Místo vydání

Cambridge

Kód UT WoS kapitoly

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