Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model-Reference-Cited by-同舟云学术

Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model

Published:2020-09-29 Issue:4 Volume:54 Page:2863-2890
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Alizamir Meysam,Kim Sungwon,Zounemat-Kermani Mohammad,Heddam Salim,Shahrabadi Amin Hasanalipour,Gharabaghi Bahram^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-020-09915-5.pdf

Reference71 articles.

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2. Alizamir M, Kim S, Kisi O, Zounemat-Kermani M (2020a) Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables. Hydrol Sci J (Accepted). https://doi.org/10.1080/02626667.2020.1735639

3. Alizamir M, Kisi O et al (2020c) Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS ONE 15(4):e0231055

4. Alizamir M, Kisi O et al (2020d) Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta Geophys 2020:1–14

5. Alizamir M, Kim S, Kisi O, Zounemat-Kermani M (2020b) A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: case studies of the USA and Turkey regions. Energy: 117239

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