Ocean Prediction System Using Future Learning of Water Temperature Time Series Prediction Data Using Deep Learning

摘要

The present invention relates to an ocean prediction system through future point data assimilation of water temperature time series prediction data using deep learning, comprising: a data collection unit for collecting water temperature time series data; A database unit for storing the collected data; A generator for generating water temperature prediction data through learning by inputting the stored water temperature time series data into the deep learning model; A data assimilation application unit that generates an improved initial field using the prediction data generated by the deep learning model and the numerical model forecast field; And an output unit for outputting location-based 2D water temperature information, which is a result value of the data animation application unit. According to the present invention as described above, the prediction data generated by the deep learning model is linked with the data assimilation technique to provide the data to which the improved initial field is applied using the prediction data generated by the deep learning model and the numerical model forecast field. It has the effect of maximizing prediction accuracy and parts where it is impossible to generate an initial value through data assimilation of the viewpoint.