Volume 17, Issue 1 (2015)                   JAST 2015, 17(1): 49-62 | Back to browse issues page

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Khoshnevisan B, Rafiee S, Iqbald J, Shamshirbande S, Omid M, Anuarf N B et al . A Comparative Study between Artificial Neural Networks and Adaptive Neuro-fuzzy Inference Systems for Modeling Energy Consumption in Greenhouse Tomato Production- A Case Study in Isfahan Province. JAST. 17 (1) :49-62
URL: http://journals.modares.ac.ir/article-23-4013-en.html
1- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
2- cDepartment of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
3- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
4- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
5- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
Abstract:   (6080 Views)
In this study greenhouse tomato production was investigated from energy consumption and greenhouse gas (GHG) emission point of views. Moreover, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) were employed to model energy consumption for greenhouse tomato production. Total energy input and output were calculated as 1316.14 and 281.1 GJ/ha. Among the all energy inputs natural gas and electricity had the most significant contribution to the total energy input. Evaluations of GHG emission illustrated that the total GHG emission was estimated at 34758.11 kg CO2eq./ha and among all inputs, electricity played the most important role, followed by natural gas. Drawing a comparison between ANN and ANFIS models demonstrated that the ANFIS-based models due to employing fuzzy rules can model output energy more accurate than ANN models. Accordingly, Correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for the best ANFIS architecture were calculated as 0.983, 0.025 and 0.149, respectively while these performance parameters for the best ANN model was computed as 0.933, 0.05414 and 0.279, respectively. 
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Article Type: Research Paper | Subject: Agricultural Machinery
Received: 2013/04/15 | Accepted: 2014/07/2 | Published: 2015/01/1

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