Spatial estimation of daily precipitation in Thailand based on infrared satellite images using artificial neural networks-Reference-Cited by-同舟云学术

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Spatial estimation of daily precipitation in Thailand based on infrared satellite images using artificial neural networks

Published:2023-08-01 Issue:1-2 Volume:154 Page:403-412
ISSN:0177-798X
Container-title:Theoretical and Applied Climatology
language:en
Short-container-title:Theor Appl Climatol

Author:

Inkeaw Papangkorn,Wongsaijai Ben,Poochinapan Kanyuta,Oonariya Chalump,Chaijaruwanich Jeerayut

Funder

Faculty of Science, Chiang Mai University

Chiang Mai University

Centre of Excellence in Mathematics, Mahidol University

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00704-023-04562-6.pdf

Reference19 articles.

1. Arab Amiri M, Mesgari MS (2017) Modeling the spatial and temporal variability of precipitation in northwest Iran. Atmosphere 8(12):254. https://www.mdpi.com/2073-4433/8/12/254

2. Barrios A, Trincado G, Garreaud R (2018) Alternative approaches for estimating missing climate data: application to monthly precipitation records in south-central Chile. Forest Ecosystems 5(1):28. https://doi.org/10.1186/s40663-018-0147-x

3. Chen D, Ou T, Gong L et al (2010) Spatial interpolation of daily precipitation in China: 1951–2005. Advan Atmospheric Sci 27(6):1221–1232. https://doi.org/10.1007/s00376-010-9151-y

4. Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10(3):209–222. https://doi.org/10.1007/s10333-012-0319-1

5. Chutsagulprom N, Chaisee K, Wongsaijai B et al (2022) Spatial interpolation methods for estimating monthly rainfall distribution in Thailand. Theor Appl Climatology 148(1):317–328. https://doi.org/10.1007/s00704-022-03927-7

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