Analysis of rainfall and temperature using deep learning model-Reference-Cited by-同舟云学术

Analysis of rainfall and temperature using deep learning model

Published:2023-05-31 Issue:1-2 Volume:153 Page:755-770
ISSN:0177-798X
Container-title:Theoretical and Applied Climatology
language:en
Short-container-title:Theor Appl Climatol

Author:

Choudhary Surendra Singh^ORCID,Ghosh S. K.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00704-023-04493-2.pdf

Reference43 articles.

1. Alotaibi K, Ghumman AR, Haider H, Ghazaw YM, Shafiquzzaman M (2018) Future predictions of rainfall and temperature using GCM and ANN for arid regions: a case study for the Qassim Region, Saudi Arabia. Water 10(9):1260. https://doi.org/10.3390/w10091260

2. Al-Shabandar R, Jaddoa A, Liatsis P, Hussain AJ (2020) A deep gated recurrent neural network for petroleum production forecasting. Mach Learn Appl 3:100013

3. Bang S, Bishnoi R, Chauhan AS, Dixit AK, Chawla I (2019) Fuzzy logic-based crop yield prediction using temperature and rainfall parameters predicted through ARMA, SARIMA, and ARMAX models. In 2019 Twelfth International Conference on Contemporary Computing (IC3). IEEE:1–6. https://doi.org/10.1109/IC3.2019.8844901

4. Bauer S, Linke HJ, Wagner M (2020) Combining industrial and urban water-reuse concepts for increasing the water resources in water-scarce regions. Water Environ Res 92(7):1027–1041. https://doi.org/10.1002/wer.1298

5. Cheng M, Fang F, Kinouchi T, Navon IM, Pain CC (2020) Long lead-time daily and monthly streamflow forecasting using machine learning methods. J Hydrol 590:125376. https://doi.org/10.1016/j.jhydrol.2020.125376

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