Predicting Tilapia Productivity in Geothermal Ponds: A Genetic Algorithm Approach for Sustainable Aquaculture Practices

Vadim Tynchenko (), Oksana Kukartseva, Yadviga Tynchenko, Vladislav Kukartsev, Tatyana Panfilova, Kirill Kravtsov, Xiaogang Wu and Ivan Malashin ()
Additional contact information
Vadim Tynchenko: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Oksana Kukartseva: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Yadviga Tynchenko: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Vladislav Kukartsev: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Tatyana Panfilova: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Kirill Kravtsov: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Xiaogang Wu: School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China
Ivan Malashin: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia

Sustainability, 2024, vol. 16, issue 21, 1-22

Abstract: This study presents a case focused on sustainable farming practices, specifically the cultivation of tilapia (Mozambican and aureus species) in ponds with geothermal water. This research aims to optimize the hydrochemical regime of experimental ponds to enhance the growth metrics and external characteristics of tilapia breeders. The dataset encompasses the hydrochemical parameters and the fish feeding base from experimental geothermal ponds where tilapia were cultivated. Genetic algorithms (GA) were employed for hyperparameter optimization (HPO) of deep neural networks (DNN) to enhance the prediction of fish productivity in each pond under varying conditions, achieving an R 2 score of 0.94. This GA-driven HPO process is a robust method for optimizing aquaculture practices by accurately predicting how different pond conditions and feed bases influence the productivity of tilapia. By accurately determining these factors, the model promotes sustainable practices, improving breeding outcomes and maximizing productivity in tilapia aquaculture. This approach can also be applied to other aquaculture systems, enhancing efficiency and sustainability across various species.

Keywords: sustainable aquaculture; genetic algorithms; hydrochemical optimization; tilapia farming; deep neural networks (DNNs) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/21/9276/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/21/9276/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:21:p:9276-:d:1506637

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().