Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network-Reference-Cited by-同舟云学术

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Published:2018-01 Issue:1 Volume:12 Page:738-749
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Taherei Ghazvinei Pezhman¹^ORCID,Hassanpour Darvishi Hossein²,Mosavi Amir³⁴⁵,Yusof Khamaruzaman bin Wan⁶,Alizamir Meysam⁷,Shamshirband Shahaboddin⁸⁹^ORCID,Chau Kwok-wing¹⁰

Affiliation:

1. Young Researchers and Elite Club, Islamic Azad University, Tehran, Iran

2. Department of Civil Engineering, Engineering and Management of Water Resources, Shahr-e-Qods Brach, Islamic Azad University, Tehran, Iran

3. Institute of Structural Mechanics, Bauhaus University Weimar, Weimar, Germany

4. Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary

5. Institute of Advanced Studies Koszeg, IASK, Koszeg, Hungary

6. Civil and Engineering Department, Universiti Teknologi Petronas, Perak, Malaysia

7. Young Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran

8. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

9. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

10. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

Funder

Foundation for Science and Technology Development of Ton Duc Thang University (FOSTECT)

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2018.1526119

Reference46 articles.

1. CROSPAL, software that uses agronomic expert knowledge to assist modules selection for crop growth simulation

2. Modeling interfacial tension in N2/n-alkane systems using corresponding state theory: Application to gas injection processes

3. A new hybrid artificial neural networks for rainfall–runoff process modeling

4. A rigorous approach for determining interfacial tension and minimum miscibility pressure in paraffin-CO2 systems: Application to gas injection processes

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