Spatial prediction using random forest spatial interpolation with sample augmentation: a case study for precipitation mapping-Reference-Cited by-同舟云学术

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Spatial prediction using random forest spatial interpolation with sample augmentation: a case study for precipitation mapping

Published:2023-01-26 Issue:1 Volume:16 Page:863-875
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Sijia Jiao,Tianjun Wu,Jiancheng Luo,Ya’nan Zhou,Wen Dong,Changpeng Wang,Shiying Dong

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-023-00936-6.pdf

Reference31 articles.

1. Accion A, Arguello F, Heras DB (2020) Dual-window Superpixel data augmentation for hyperspectral image classification. Appl Sci-Basel 10(24):8833. https://doi.org/10.3390/app10248833

2. Alvarez O, Guo Q, Klinger RC, Li W, Doherty P (2014) Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation. Int J Climatol 34(7):2258–2268. https://doi.org/10.1002/joc.3835

3. Behrens T, Schmidt K, RAV R, Gries P, Scholten T, RA MM (2018) Spatial modelling with Euclidean distance fields and machine learning. Eur J Soil Sci 69(5):757–770. https://doi.org/10.1111/ejss.12687

4. Berndt C, Rabiei E, Haberlandt U (2014) Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. J Hydrol 508:88–101. https://doi.org/10.1016/j.jhydrol.2013.10.028

5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

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