Optimized landslide susceptibility mapping and modelling using PS-InSAR technique: a case study of Chitral valley, Northern Pakistan-Reference-Cited by-同舟云学术

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Optimized landslide susceptibility mapping and modelling using PS-InSAR technique: a case study of Chitral valley, Northern Pakistan

Published:2021-05-17 Issue: Volume: Page:1-22
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Hussain Sajid¹,Hongxing Sun¹,Ali Muhammad¹,Sajjad Meer Muhammad²,Ali Muhammad¹,Afzal Zeeshan¹,Ali Sajid³

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

2. Institute of Geographical Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Science (CAS), Beijing, China

3. School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.1914750

Reference49 articles.

1. Landslide susceptibility hazard map in southwest Sweden using artificial neural network

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

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

4. Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan

5. Indicator-based approach for assigning physical vulnerability of the houses to landslide hazard in the Himalayan region of India

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1. Susceptibility evaluation of highway landslide disasters based on SBAS-InSAR: a case study of S211 highway in Lanping County;Natural Hazards;2024-09-12

2. The generative adversarial neural network with multi-layers stack ensemble hybrid model for landslide prediction in case of training sample imbalance;Stochastic Environmental Research and Risk Assessment;2024-04-26

3. Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan;Remote Sensing;2024-03-12

4. Regional scale landslide risk assessment using field and remote sensing data in a semi-quantitative approach in Chitral, northern Pakistan;2024-01-18

5. Standing on the shoulder of a giant landslide: A six-year long InSAR look at a slow-moving hillslope in the western Karakoram;Geomorphology;2024-01

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