Comparison of landslide susceptibility models and their robustness analysis: a case study from the NW Himalayas, Pakistan-Reference-Cited by-同舟云学术

Comparison of landslide susceptibility models and their robustness analysis: a case study from the NW Himalayas, Pakistan

Published:2021-12-22 Issue: Volume: Page:1-38
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Ikram Nawaz¹,Basharat Muhammad¹,Ali Asghar²,Usmani Nadeem Ahmad³⁴,Gardezi Syed Ahsan Hussain³,Hussain Mian Luqman⁵,Riaz Muhammad Tayyib¹⁶

Affiliation:

1. Institute of Geology, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan

2. Department of Geology, University of Peshawar, Peshawar, Pakistan

3. Directorate of Azad Jammu and Kashmir, Geological Survey of Pakistan, Muzaffarabad, Pakistan

4. Key Laboratory of Mountain Hazards and Earth Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China

5. National Centre of Excellence in Geology, University of Peshawar, Peshawar, Pakistan

6. Department of Geology, University of Azad Jammu and Kashmir, Athmuqam, Pakistan

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.2017010

Reference137 articles.

1. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia

2. Tectonic imprints of the Hazara Kashmir Syntaxis on the Northwest Himalayan fold and thrust belt, North Pakistan

3. Tectonic Imprints of the Hazara Kashmir Syntaxis on the Mesozoic Rocks Exposed in Munda, Mohmand Agency, Northwest Pakistan

4. Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan

5. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

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