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Gholami Parashkoohi, Mohammad, Rasooli, Mohsen, Beheshti, Babak, Ghahdarijani, Mohamad. (1401). Modeling energy use and economic productivity of different fish production systems using artificial neural networks. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, (), -.
Mohammad Gholami Parashkoohi; Mohsen Rasooli; Babak Beheshti; Mohamad Ghahdarijani. "Modeling energy use and economic productivity of different fish production systems using artificial neural networks". نشریه علمی پژوهشی دانشگاه آزاد اسلامی, , , 1401, -.
Gholami Parashkoohi, Mohammad, Rasooli, Mohsen, Beheshti, Babak, Ghahdarijani, Mohamad. (1401). 'Modeling energy use and economic productivity of different fish production systems using artificial neural networks', نشریه علمی پژوهشی دانشگاه آزاد اسلامی, (), pp. -.
Gholami Parashkoohi, Mohammad, Rasooli, Mohsen, Beheshti, Babak, Ghahdarijani, Mohamad. Modeling energy use and economic productivity of different fish production systems using artificial neural networks. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 1401; (): -.

Modeling energy use and economic productivity of different fish production systems using artificial neural networks

International Journal of Agricultural Management and Development
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 اسفند 1401
نوع مقاله: Research Article
نویسندگان
Mohammad Gholami Parashkoohi* 1؛ Mohsen Rasooli2؛ Babak Beheshti3؛ Mohamad Ghahdarijani4
1Associate Professor, of Department of Biosystem Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran
2گروه آموزشی مکانیک بیوسیستم دانشگاه آزاد تاکستان
3گروه آموزشی مکانیزاسیون کشاورزی دانشگاه علوم تحقیقات تهران
4Department of Agricultural Systems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
چکیده
This study aimed to use artificial neural networks (ANNs) to predict output energy and economic indicators. So, data on two fish breeding sites were collected with a questionnaire, directly from the site owners and administrators, and from the records. Then, the input and output energy of cold-water and hot-water fish were calculated. The cold-water fish were found to have a more favorable energy ratio (ER) (2.24), energy productivity (EP) (0.04 kg MJ−1), specific energy (SE) (26.83 MJ kg-1), and net energy gain (NEG) (33222.16 MJ kg-1). According to the results, fish feed and electricity are two factors among energy consumption inputs whose proper management will increase energy efficiency. The benefit-to-cost ratio was positive for cold-water fish (1.54) and hot-water fish (2.45). The productivity of cold-water and hot-water fish was 0.58 kg $-1 and 0.52 kg $-1, respectively. The results of ANNs showed that R2 varied from 0.947 to 0.993 overall, from 0.912 to 0.964 for the training stage, and from 0.978 to 0.980 for the testing stage in the case of cold-water fish. Regarding hot-water fish, these values were 0.885-0.998, 0.923-0.952, and 0.952-0.995, respectively. So, ANNs can be used to predict output energy and economic productivity.
کلیدواژه‌ها
Energy use؛ Fish؛ Modeling؛ Productivity
آمار
تعداد مشاهده مقاله: 65


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