Proposal and Implementation of Seawater Temperature Prediction Model using Transfer Learning Considering Water Depth Differences

Aquaculture is one of the most important industries worldwide, and most marine products are produced by aquaculture.On the other hand, the aquaculture farmers are faced on the challenge of damage to marine products due to abnormal seawater temperatures.Research on seawater temperature prediction have been conducted, but many of them require a large amount of training data.Collecting seawater temperature data is not easy, and it takes an enormous time to introduce in new aquaculture farms.Therefore, the purpose of this study is to predict seawater temperature even with a small amount of training data for about one year.In this paper, we propose a seawater temperature prediction model using transition learning.The proposed model also attempts to improve the prediction accuracy by considering the difference in water depth between observation points.The results of the evaluation experiment showed that the prediction accuracy can be improved by transfer learning when learning with a small amount of data.In addition, we also confirmed that adding water depth values to the input layer may not lead to improved prediction accuracy for transfer learning.