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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Ghazvinei:2018:eaCFM,

• author = "Pezhman Taherei Ghazvinei and Hossein Hassanpour Darvishi and Amir Mosavi and Khamaruzaman {bin Wan Yusof} and Meysam Alizamir and Shahaboddin Shamshirband and Kwok-wing Chau",
• title = "Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network",
• journal = "Engineering Applications of Computational Fluid Mechanics",
• year = "2018",
• volume = "12",
• number = "1",
• pages = "738--749",
• keywords = "genetic algorithms, genetic programming, sustainable production, sugar cane, machine learning, growth model, estimation, extreme learning machine, prediction",
• publisher = "Taylor \& Francis",
• ISSN = "19942060",
• bibsource = "OAI-PMH server at www.db-thueringen.de",
• language = "eng",
• oai = "oai:www.db-thueringen.de:dbt_mods_00037448",
• rights = "https://creativecommons.org/licenses/by-nc/4.0/; info:eu-repo/semantics/openAccess",
• URL = "https://doi.org/10.1080/19942060.2018.1526119",
• URL = "https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20181017-38129",
• URL = "https://www.db-thueringen.de/receive/dbt_mods_00037448",
• URL = "https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00043598/Mosavi_Amir_Sugarcane%20growth%20prediction.pdf",
• DOI = "doi:10.1080/19942060.2018.1526119",
• size = "13 pages",
• 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 (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalisation 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.",
• notes = "Also known as \cite{oai:www.db-thueringen.de:dbt_mods_00037448}",

}

Genetic Programming entries for Pezhman Taherei Ghazvinei Hossein Hassanpour Darvishi Amir Mosavi Khamaruzaman bin Wan Yusof Meysam Alizamir Shahaboddin Shamshirband Kwok-Wing Chau

Citations