TY  - JOUR
A1  - Ghazvinei, Pezhman Taherei
A1  - Darvishi, Hossein Hassanpour
A1  - Mosavi, Amir
A1  - Yusof, Khamaruzaman bin Wan
A1  - Alizamir, Meysam
A1  - Shamshirband, Shahaboddin
A1  - Chau, Kwok-Wing
T1  - Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - 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.
KW  - Künstliche Intelligenz
KW  - Sustainable production
KW  - ELM
KW  - prediction
KW  - machine learning
KW  - sugarcane
KW  - estimation
KW  - growth mode
KW  - extreme learning machine
KW  - OA-Publikationsfonds2018
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181017-38129
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2018.1526119
VL  - 2018
IS  - 12,1
SP  - 738
EP  - 749
PB  - Taylor & Francis
ER  -