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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China...

Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_54c5590592bc49b4b1dc9c57a49b598b

Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway

About this item

Full title

Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway

Publisher

Basel: MDPI AG

Journal title

Land (Basel), 2025-01, Vol.14 (1), p.172

Language

English

Formats

Publication information

Publisher

Basel: MDPI AG

More information

Scope and Contents

Contents

The effectiveness of data-driven landslide susceptibility mapping relies on data integrity and advanced geospatial analysis; however, selecting the most suitable method and identifying key regional factors remains a challenging task. To address this, this study assessed the performance of six machine learning models, including Convolutional Neural...

Alternative Titles

Full title

Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_54c5590592bc49b4b1dc9c57a49b598b

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_54c5590592bc49b4b1dc9c57a49b598b

Other Identifiers

ISSN

2073-445X

E-ISSN

2073-445X

DOI

10.3390/land14010172

How to access this item