Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80296
Title: Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network
Authors: Ghazvinei, PT
Darvishi, HH
Mosavi, A
Yusof, KB
Alizamir, M
Shamshirband, S
Chau, KW 
Keywords: Sustainable production
Sugarcane
Machine learning: growth model
Estimation
Extreme learning machine
Prediction
Issue Date: 2018
Publisher: Hong Kong Polytechnic University, Department of Civil and Structural Engineering
Source: Engineering applications of computational fluid mechanics, Sept 2018, v. 12, no. 1, p. 738-749 How to cite?
Journal: Engineering applications of computational fluid mechanics 
Abstract: 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 (R-2) 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.
URI: http://hdl.handle.net/10397/80296
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2018.1526119
Rights: © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Ghazvinei, P.T., Darvishi, H.H., Mosavi, A., Yusof, K.B., Alizamir, M., Shamshirband, S., & Chau, K.W. (2018). Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Engineering applications of computational fluid mechanics, 12 (1), 738-749 is available at https://dx.doi.org/10.1080/19942060.2018.1526119
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