Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms-Reference-Cited by-同舟云学术

Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms

Published:2021-01-01 Issue:1 Volume:12 Page:3381-3408
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Abraham Minu Treesa¹^ORCID,Satyam Neelima¹^ORCID,Jain Prashita¹,Pradhan Biswajeet²³^ORCID,Alamri Abdullah⁴

Affiliation:

1. Department of Civil Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

2. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

3. Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Malaysia

4. Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia

Funder

King Saud University

University of Technology Sydney

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2021.2011791

Reference63 articles.

1. Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

2. Runout modeling and calibration of friction parameters of Kurichermala debris flow, India

3. Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning

4. Forecasting landslides using SIGMA model: a case study from Idukki, India

5. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey

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