Prediction of daily chlorophyll-a concentration in rivers by water quality parameters using an efficient data-driven model: online sequential extreme learning machine-Reference-Cited by-同舟云学术

Prediction of daily chlorophyll-a concentration in rivers by water quality parameters using an efficient data-driven model: online sequential extreme learning machine

Published:2021-09-29 Issue:6 Volume:69 Page:2339-2361
ISSN:1895-6572
Container-title:Acta Geophysica
language:en
Short-container-title:Acta Geophys.

Author:

Alizamir Meysam,Heddam Salim,Kim Sungwon,Gorgij Alireza Docheshmeh,Li Peiyue,Ahmed Kaywan Othman,Singh Vijay P.

Publisher

Springer Science and Business Media LLC

Subject

Geophysics

Link

https://link.springer.com/content/pdf/10.1007/s11600-021-00678-3.pdf

Reference64 articles.

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3. Alizamir M, Kim S, Zounemat-Kermani M, Heddam S, Kim NW, Singh VP (2020c) Kernel extreme learning machine: an efficient model for estimating daily dew point temperature using weather data. Water 12(9):2600

4. Alizamir M, Kim S, Zounemat-Kermani M, Heddam S, Shahrabadi AH, Gharabaghi B (2021b) Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model. Artif Intell Rev 54(4):2863–2890

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 197:117239

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