@article{Okuno_Otsuka_2020, title={How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7216}, DOI={10.1609/aaai.v34i10.7216}, abstractNote={<p>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 <sup>◦</sup>C and daily prediction of up to one week has an average error of about 0.2 to 0.5 <sup>◦</sup>C. This is enough to meet actual worker need, which is within 1 <sup>◦</sup>C error, thus confirming that our seawater prediction method is suitable for actual sites.</p>}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Okuno, Masahito and Otsuka, Takanobu}, year={2020}, month={Apr.}, pages={13887-13888} }