Comparison of different artificial intelligence-based models for prediction of dissolved oxygen rates at fish growing pools-Case Study: Ahvaz city

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

AFZALI S.M.J.; SHEIKH DAVOODI M.J.; MONTAZER HOJAT A.H.; SEYED MORTEZAEI S.R.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF RESEARCHES IN MECHANICS OF AGRICULTURAL MACHINERY Year:2018 | Volume:7 | Issue:1 (12) Page(s): 69-80

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

444

Download:

Cites:

Information Journal Paper

Title

Comparison of different artificial intelligence-based models for prediction of dissolved oxygen rates at fish growing pools-Case Study: Ahvaz city

Author(s)

AFZALI S.M.J. | SHEIKH DAVOODI M.J. | MONTAZER HOJAT A.H. | SEYED MORTEZAEI S.R. | Issue Writer Certificate

Keywords

Adaptive Nero Fuzzy Inference System

Genetic Algorithm

Neural Network

ANFIS

ANNDissolved Oxygen

Abstract

High energy consumption and worker costs caused to fish growers DOnt have highly motivated to buy aerators. Dissolved Oxygen (DO) is one of the importance parameters which can be applied for time of starting and finishing activity of aerators however its sensor is expensive and should be estimated by other parameters. The aim of this research was to create a suitable model to estimate DO rate at growing pools. Input parameters were pH and temperatures of water, moisture and temperature of air and wind speed. Along growing period total of parameters were measured and with three models were estimated. The first one was Artificial Neural Network (ANN). The results showed that the maximum R2 was achieved by logsig-purelin transfer function with 17 neuron at hidden layer with 0. 70. Second one was an aggreGAted ANN-AG model and its result showed that the maximum of R2 was 0. 41 and finally, Third one was Adaptive Nero Fuzzy Inference System (ANFIS) model and the results showed that at condition of 3 Membership function per layer with GAussian type and constant output could estimate DO with R=0. 87 accuracy. Therefore ANN was the best method for evaluation of DO at growing pools for the region under study.

Cites

No record.

References

No record.

Cite

APA: Copy

AFZALI, S.M.J., SHEIKH DAVOODI, M.J., SEYED MORTEZAEI, S.R., & MONTAZER HOJAT, A.H.. (2018). Comparison of different artificial intelligence-based models for prediction of dissolved oxygen rates at fish growing pools-Case Study: Ahvaz city. JOURNAL OF RESEARCHES IN MECHANICS OF AGRICULTURAL MACHINERY, 7(1 (12) ), 69-80. SID. https://sid.ir/paper/270402/en

Vancouver: Copy

AFZALI S.M.J., SHEIKH DAVOODI M.J., SEYED MORTEZAEI S.R., MONTAZER HOJAT A.H.. Comparison of different artificial intelligence-based models for prediction of dissolved oxygen rates at fish growing pools-Case Study: Ahvaz city. JOURNAL OF RESEARCHES IN MECHANICS OF AGRICULTURAL MACHINERY[Internet]. 2018;7(1 (12) ):69-80. Available from: https://sid.ir/paper/270402/en

IEEE: Copy

S.M.J. AFZALI, M.J. SHEIKH DAVOODI, S.R. SEYED MORTEZAEI, and A.H. MONTAZER HOJAT, “Comparison of different artificial intelligence-based models for prediction of dissolved oxygen rates at fish growing pools-Case Study: Ahvaz city,” JOURNAL OF RESEARCHES IN MECHANICS OF AGRICULTURAL MACHINERY, vol. 7, no. 1 (12) , pp. 69–80, 2018, [Online]. Available: https://sid.ir/paper/270402/en

Related Journal Papers