Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250021" target="_blank" >RIV/61989100:27240/22:10250021 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2072-4292/14/13/3029" target="_blank" >https://www.mdpi.com/2072-4292/14/13/3029</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs14133029" target="_blank" >10.3390/rs14133029</a>

Result language

angličtina

Original language name

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Original language description

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value &gt; 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

Remote Sensing

ISSN

2072-4292

e-ISSN

—

Volume of the periodical

14

Issue of the periodical within the volume

13

Country of publishing house

CH - SWITZERLAND

Number of pages

48

Pages from-to

nestrankovano

UT code for WoS article

000824449100001

EID of the result in the Scopus database

2-s2.0-85133289824