Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01151-z.pdf
Reference86 articles.
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2. Ada M, San BT (2018) Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey. Nat Hazards 90:237–263. https://doi.org/10.1007/s11069-017-3043-8
3. Adnan MSG, Rahman S, Ahmed N, Ahmed B, Rabbi M, Rahman M (2020) Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping. Remote Sens (basel) 12:3347. https://doi.org/10.3390/rs12203347
4. Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22:717–727
5. Akinci H, Zeybek M (2021) Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey. Nat Hazards 108:1515–1543. https://doi.org/10.1007/s11069-021-04743-4
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1. Assessing the relationship between landslide susceptibility and land cover change using machine learning;Vietnam Journal of Earth Sciences;2024-05-02
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