A comparative assessment of the ability of different types of machine learning in short-term predictions of nocturnal frosts-Reference-Cited by-同舟云学术

A comparative assessment of the ability of different types of machine learning in short-term predictions of nocturnal frosts

Published:2024-02-05 Issue:4 Volume:72 Page:2955-2973
ISSN:1895-7455
Container-title:Acta Geophysica
language:en
Short-container-title:Acta Geophys.

Author:

Mesgari Ebrahim,Mahmoudi Peyman^ORCID,Kord Tamandani Yahya,Tavousi Taghi,Amir Jahanshahi Seyed Mahdi

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11600-023-01276-1.pdf

Reference85 articles.

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3. Alijani B, Mahmoudi P, Rigi Chahi AB, Khosravi P (2010) Investigation of the persistence of frost days in Iran using chain Markov model. Phys Geogr Res Q 42(73):1–19 (In Persian)

4. Alizamir M, Kisi O, Adnan RM, Kuriqi A (2020) Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta Geophys 68:1113–1126. https://doi.org/10.1007/s11600-020-00446-9

5. Anfossi D, Bacci P, Longhetto A (1976) Forecasting of vertical temperature profiles in the atmosphere during nocturnal radiation inversions from air temperature trend at screen height. Q J R Meteorol Soc 102(431):173–180. https://doi.org/10.1002/qj.49710243114

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