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http://dx.doi.org/10.6109/jkiice.2022.26.3.374

Prediction of Sea Water Condition Changes using LSTM Algorithm for the Fish Farm  

Rijayanti, Rita (Department of Information and Communication Engineering, Changwon National University)
Hwang, Mintae (Department of Information and Communication Engineering, Changwon National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.3, 2022 , pp. 374-380 More about this Journal
Abstract
This paper shows the results of a study that predicts changes in seawater conditions in sea farms using machine learning-based long short term memory (LSTM) algorithms. Hardware was implemented using dissolved oxygen, salinity, nitrogen ion concentration, and water temperature measurement sensors to collect seawater condition information from sea farms, and transferred to a cloud-based Firebase database using LoRa communication. Using the developed hardware, seawater condition information around fish farms in Tongyeong and Geoje was collected, and LSTM algorithms were applied to learning results using these actual datasets to obtain predictive results showing 87% accuracy. Flask and REST APIs were used to provide users with predictive results for each of the four parameters, including dissolved oxygen. These predictive results are expected to help fishermen reduce significant damage caused by fish group death by providing changes in sea conditions in advance.
Keywords
Sea Fish Farm; Sea Water Condition; LSTM Algorithm; Condition Changes Prediction; Flask and REST API;
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  • Reference
1 Recurrent Neural Network Tutorial Part1-Introduction to RNNs [Internet]. Available: https://m.blog.naver.com/PostView.naver?isHttpsRedirect=true&blogId=rkdwnsdud555&logNo=221222845536.
2 J. Zhang, Z. Zhang, Y. Weng, S. Gosling, H. Yang, C. Yang, W. Le, and Q. Ma, "Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoir," in Proceeding IEEE 44th Annual Computer, Software, and Applications Conference (COMPSAC), Spain : Madrid, pp. 1125-1126, 2020.
3 R. Rijayanti, A. Kadam, A. B. Wahyutama, B. Lee, and M. Hwang, "Design of the Environmental Data Monitoring and Prediction System for the Fish Farms," in Proceeding of KIICE Spring Conference, Korea : Yeosu City, pp. 178-180, 2021.
4 S.A. Ludwig, "Comparison of Time Series Approaches applied to Greenhouse Gas Analysis: ANFIS, RNN, and LSTM," in Proceeding of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), USA: New Orleans, pp. 1-6, 2019.
5 A. Tamang and S. Shukla, "Water Demand Prediction Using Support Vector Machine Regression," in Proceeding of International Conference on Data Science and Communication (IconDSC), India : Bangalore, pp. 1-5, 2019.
6 S. I. Ranapurwala, J. E. Cavanaugh, T. Young, H. Wu, C. Peek-Asa, and M. R. Ramirez, "Public health application of predictive modeling: an example from farm vehicle crashes," Journal of Injury Epidemiology (Part of Springer Nature), vol. 6, no. 1, pp. 1-11, Jun. 2019.
7 A. Doni, C. Murthy and M. Z. Kurian, "Survey on multi sensor based air and water quality monitoring using IoT," Journal of Scientific Research in Science (JSRS), vol. 17, no. 2, pp. 147-153, 2018.
8 M. Ahmed, M. O. Rahaman, M. Rahman, and M. A. Kashem, "Analyzing the Quality of Water and Predicting the Suitability for Fish Farming based on IoT in the Context of Bangladesh," in Proceeding of International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka: Bangladesh, Dec. 2019.