How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)
Abstract
The increasing global demand for marine products has turned attention to marine aquaculture. In marine aquaculture, appropriate environment control is important for a stable supply. The influence of seawater temperature on this environment is significant and accurate prediction is therefore required. In this paper, we propose and describe the implementation of a seawater prediction method using data acquired from real aquaculture areas and neural networks. Our evaluation experiment showed that hourly next-day prediction has an average error of about 0.2 to 0.4 ◦C and daily prediction of up to one week has an average error of about 0.2 to 0.5 ◦C. This is enough to meet actual worker need, which is within 1 ◦C error, thus confirming that our seawater prediction method is suitable for actual sites.
Cite
Text
Okuno and Otsuka. "How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7216Markdown
[Okuno and Otsuka. "How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/okuno2020aaai-predict/) doi:10.1609/AAAI.V34I10.7216BibTeX
@inproceedings{okuno2020aaai-predict,
title = {{How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)}},
author = {Okuno, Masahito and Otsuka, Takanobu},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13887-13888},
doi = {10.1609/AAAI.V34I10.7216},
url = {https://mlanthology.org/aaai/2020/okuno2020aaai-predict/}
}