Exploring different approaches for landslide susceptibility zonation mapping in Manipur: a comparative study of AHP, FR, machine learning, and deep learning models-Reference-Cited by-同舟云学术

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Exploring different approaches for landslide susceptibility zonation mapping in Manipur: a comparative study of AHP, FR, machine learning, and deep learning models

Published:2024-06-22 Issue: Volume: Page:1-30
ISSN:1449-8596
Container-title:Journal of Spatial Science
language:en
Short-container-title:Journal of Spatial Science

Author:

Kshetrimayum Arvindd¹^ORCID,H Ramesh¹,Goyal Akash²

Affiliation:

1. Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, India

2. RRSC-North, Indian Space Research Organisation, Delhi, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/14498596.2024.2368156

Reference54 articles.

1. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

2. Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

3. Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India

4. The San Mateo County California Gis Project for Predicting the Consequences of Hazardous Geologic Processes

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