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

  • Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.

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    Gefördert aus Mitteln des Open-Access-Publikationsfonds' der Bauhaus-Universität Weimar und vom Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft (TMWWDG).

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Metadaten
Author: Pezhman Taherei Ghazvinei, Hossein Hassanpour Darvishi, Amir MosaviORCiD, Khamaruzaman bin Wan Yusof, Meysam Alizamir, Shahaboddin Shamshirband, Kwok-wing Chau
DOI (Cite-Link):https://doi.org/10.1080/19942060.2018.1526119Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20181017-38129Cite-Link
URL:https://www.tandfonline.com/doi/full/10.1080/19942060.2018.1526119
Parent Title (English):Engineering Applications of Computational Fluid Mechanics
Publisher:Taylor & Francis
Document Type:Article
Language:English
Date of Publication (online):2018/09/28
Date of first Publication:2018/09/28
Release Date:2018/10/17
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2018
Issue:12,1
First Page:738
Last Page:749
Tag:extreme learning machine
ELM; Sustainable production; estimation; growth mode; machine learning; prediction; sugarcane
GND Keyword:Künstliche Intelligenz
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
500 Naturwissenschaften und Mathematik
BKL-Classification:05 Kommunikationswissenschaft
06 Information und Dokumentation
31 Mathematik
54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2018
Licence (German):License Logo Creative Commons 4.0 - Namensnennung-Nicht kommerziell (CC BY-NC 4.0)