Designing Forecasting Parameter Algorithm of Environmental Shrimp Using Recurrent Neural Network
Abstract
With the strong development of science and technology, the study of technologies related to environmental forecasting is important. In recent years, the application of smart technology in aquaculture has been widely applied. Based on the requirement, we focus on predicting the environmental parameters applied in shrimp farming, especially white shrimp, one of the seafood grown in our country. In the paper, we exploit a small branch of identification problem. This paper proposes an algorithmic construction method to predict changes in shrimp farm environmental parameters and simulate the next parameters based on current parameters. The goal of the paper is to reduce the parameter of Recurrent Neural Network (RNN) while ensuring data accuracy. Experimental results show that the proposal algorithm improves up to 85 percent when selecting suitable learning factor of neural networks.
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01 2003.
[3] T. T. Nguyen, “Vietnam and the environment: problems and solutions,” International Journal of Environmental Studies, vol. 66, no. 1, pp. 1–8,
2009.
[4] N. Hien, “Water quality classification by artificial neural network - a case study of dong nai river, vietnam,” Vietnam Journal of Science and
Technology, vol. 55, p. 297, 03 2018.
[5] L. P. Team, “Predicting water quality with machine learning,” Sept. 2019, retrieved from official website:https://locustec.com/blog/predicting-
water-quality-with-machine-learning/.
[6] M. S. Shahriar, A. Rahman, and J. McCulloch, “Predicting shellfish farm closures using time series classification for aquaculture decision
support,” Computers and Electronics in Agriculture, vol. 102, pp. 85 –97, 2014.
[7] V. Hatje, M. Souza, L. Ribeiro, G. Ea, and F. Barros, “Detection of environmental impacts of shrimp farming through multiple lines of
evidence,” Environmental Pollution, vol. 219, 07 2016.
[8] F. Pez-Osuna, “The environmental impact of shrimp aquaculture: Causes, effects, and mitigating alternatives,” Environmental manage-
ment, vol. 28, pp. 131–40, 08 2001.
[9] J. Brownlee, Master Machine Learning Algorithms: Discover How They Work and Implement Them From Scratch. Jason
Brownlee, 2016. [Online]. Available: https://books.google.com.vn/books?id=PdZBnQAACAAJ
[10] J. Zhang, Z. Zhan, Y. Lin, N. Chen, Y. Gong, J. Zhong, H. S. H. Chung, Y. Li, and Y. Shi, “Evolutionary computation meets machine learning:
A survey,” IEEE Computational Intelligence Magazine, vol. 6, no. 4, pp. 68–75, Nov 2011.
[11] M. T. Hagan, H. B. Demuth, M. H. Beale, and O. D. Jesus, Neural Network Design. Amazon, 2014. [Online]. Available: https:
//hagan.okstate.edu/nnd.html
[12] A. Sherstinsky, “Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network,” Physica D: Nonlinear
Phenomena, vol. 404, p. 132306, Mar 2020. [Online]. Available: http://dx.doi.org/10.1016/j.physd.2019.132306
[13] P. N. Huu, V. Tran-Quang, and T. Miyoshi, “Low-complexity and energy-efficient algorithms on image compression for wireless sensor
networks,” IEICE Transactions on Communications, vol. E93-B, no. 12, pp. 3438–3447, Dec. 2010.
[14] D. P. Kumar, T. Amgoth, and C. S. R. Annavarapu, “Machine learning algorithms for wireless sensor networks: A survey,” Information Fusion,
vol. 49, pp. 1 – 25, 2019.
[15] P. N. Huu, V. Tran-Quang, and T.Miyoshi, “Multi-hop reed-solomon encoding scheme for image transmission on wireless sensor networks,”
in Proceedings of 4th Intl Conf. Commun. Electron. (ICCE 2012), Aug. 2012, pp. 74–79.
[16] M. A. Alsheikh, S. Lin, D. Niyato, and H. Tan, “Machine learning in wireless sensor networks: Algorithms, strategies, and applications,”
IEEE Communications Surveys Tutorials, vol. 16, no. 4, pp. 1996–2018, Fourthquarter 2014.
[17] V. Tran-Quang, P. N. Huu, and T. Miyoshi, “A collaborative target tracking algorithm considering energy constraint inwsns,” in 19th Intl
Conf. Software, Telecomm. Comput. Netw. (SoftCOM 2011), Sept. 2011, pp. 1–5.
[18] R. Yu, P. Leung, and P. Bienfang, “Predicting shrimp growth: Artificial neural network versus nonlinear regression models,” Aquacultural
Engineering, vol. 34, pp. 26–32, 01 2006.
[19] T. N. Duong, V. D. Than, T. H. Tran, T. H. A. Pham, V. H. A. Nguyen, and H. N. Tran, “A practical solution to the acm recsys challenge 2018,”
in 5th NAFOSTED Conf. on Infor. and Comput. Scie. (NICS 2018), Nov. 2018, pp. 341–343.
[20] X. Du, A. Youssef, Y. Wang, N. Padfield, and D. Matthews, “Detection of snapping shrimp using machine learning,” 11 2018.
[21] V. Tran-Quang, P. N. Huu, and T. Miyoshi, “Adaptive transmission range assignment algorithm for in-routing image compression on wireless
sensor networks,” in Proceedings of 3rd Intl Conf. Commun. Electron. (ICCE 2010), Aug. 2010, pp. 1–6.
[22] K. Awan, D. P. A. Shah, K. Iqbal, S. Gillani, W. Ahmad, and Y. Nam, “Underwater wireless sensor networks: A review of recent issues and
challenges,” Wireless Communications and Mobile Computing, vol. 2019, pp. 1–20, 01 2019.
[23] P. Nguyen Huu, V. Tran-Quang, and T. Miyoshi, “Video compression schemes using edge feature on wireless video sensor networks,” Journal
of Electrical and Computer Engineering, vol. 2012, pp. 1–20, Oct. 2012.
[24] R. Abd Rahman, G. Kendall, R. Ramli, Z. Jamari, and K. Ku-Mahamud, “Shrimp feed formulation via evolutionary algorithm with power heuris-
tics for handling constraints,” Complexity, vol. 2017, pp. 1–12, 11 2017.
Published
2020-12-29
How to Cite
NGUYEN, Phat Huu et al.
Designing Forecasting Parameter Algorithm of Environmental Shrimp Using Recurrent Neural Network.
Journal of Science and Technology: Issue on Information and Communications Technology, [S.l.], v. 18, n. 12.2, p. 8-14, dec. 2020.
ISSN 1859-1531.
Available at: <http://ict.jst.udn.vn/index.php/jst/article/view/104>. Date accessed: 19 apr. 2024.
doi: https://doi.org/10.31130/ict-ud.2020.104.
Section
Articles
