Honey Production Modeling in Turkey, China and Iran by Artificial Neural Networks

Alaeddin Yoruk (), Åenol Celik () and Dervis Topuz ()
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Alaeddin Yoruk: Osmaniye Korkut Ata University / Turkiye
Åenol Celik: Bingol University / Turkiye
Dervis Topuz: Nigde Ã-mer Halis University / Turkiye

Journal of Original Studies, 2023, vol. 4, issue 1, 35-46

Abstract: The aim of this study is to model and predict the amount of honey production by artificial neural networks (ANN) in China, Turkey and Iran, where the most honey is produced in the world. The study covers the data for the period of 1961-2021 for Turkey, 1961-2020 for China and Iran. In the ANN method, the Hyperbolic Tangent Function was used as the activation function. The effectiveness of the developed model was determined by statistics such as Mean Error Squares (MSE) and Mean Average Error (MAE). MSE values for honey production modeling in China, Turkey and Iran were 658081803, 21877686 and 11754352, respectively, while MAE values were 19982, 3803 and 2854, respectively. According to the foresight results obtained by ANN, honey production is expected to be 536651-543767 tons in China between 2021-2030, 77486-81501 tons in Iran, and 106772-112778 tons in Turkey between 2022-2030. It is hoped that the amount of honey production in these countries will continue with ups and downs and will be more than today's production. It has been seen that the ANN method gives successful results in production modeling.

Keywords: Artificial Neural Networks; Activation Function; Honey (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:jle:joujos:jos1926

DOI: 10.47243/jos.1926

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