Comment on Saha, S., Saha, A., Roy, B. et al. (2022a) Integrating the particle swarm optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model. Earth Sci Inform 15, 2637–2662. https://doi.org/10.1007/s12145-022-00878-5 and Saha, S., Saha, A., Roy, B. et al. (2022b) correction to: Integrating the particle swarm optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model. Earth Sci Inform 15, 2663–2664 https://doi.org/10.1007/s12145-022-00892-7
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01046-z.pdf
Reference12 articles.
1. Ajaykumar BN, Gopinath G (2018) Geospatial techniques for the analysis of hypsometric parameters of a humid tropical river basin, south Western Ghats, India. Carpathian J Earth Environ Sci 13(2):465–476
2. Kumar BA, Gopinath G, Chandran MS (2014) River sinuosity in a humid tropical river basin, south west coast of India. Arab J Geosci 7:1763–1772
3. Ramasamy SM, Gunasekaran S, Saravanavel J, Joshua RM, Rajaperumal R, Kathiravan R, Palanivel K, Muthukumar M (2021) Geomorphology and landslide proneness of Kerala, India A geospatial study. Landslides 18:1245–1258
4. Saha S, Saha A, Roy B et al (2022a) Integrating the particle swarm optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model. Earth Sci Inform 15:2637–2662. https://doi.org/10.1007/s12145-022-00878-5. )
5. Saha S, Saha A, Roy B et al (2022b) Correction to: integrating the particle swarm optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model. Earth Sci Inform 15:2663–2664. https://doi.org/10.1007/s12145-022-00892-7. )
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