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

• Published in: 한국정보통신학회논문지 Vol. 26; no. 3; pp. 374 - 380
• Main Authors: Rita Rijayanti(리타 리자얀티), Mintae Hwang(황민태)
• Format: Journal Article
• Language: English
• Published: 한국정보통신학회 01.03.2022
• Subjects: 전자/정보통신공학; 상태 변화 예측; Flask와 REST API; Condition Changes Prediction; 해수 상태; Sea Water Condition; LSTM 알고리즘; LSTM Algorithm; Sea Fish Farm; Flask and REST API; 바다 양식장
• ISSN: 2234-4772; 2288-4165
• DOI: 10.6109/jkiice.2022.26.3.374

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. KCI Citation Count: 0