A Study on the Prediction Algorithm of Sea Fog Dissipation based on Machine Learning
Since 2016, KHOA (Korea Hydrographic and Oceanographic Agency) has managed and performed challengeable researches to predict the occurrence of sea fog using Deep Neural Network (DNN), and found its feasibility and applicability to real operation of trading ports in the Republic of Korea. Currently, DNN-based sea fog occurrence prediction is in operation experimentally at six ports. However, the prediction of sea fog dissipation is much more difficult due to not only complexity of sea fog dissipation compared with its occurrence, but also each dissipative condition of various sea fog types.
To predict hourly dissipation of sea fog using DNN, we constructed training data sets of air temperature, sea water temperature, atmospheric pressure, air-sea temperature difference, humidity, wind speed and direction, and visibility, which are obtained only after the sea fog dissipation. We investigated the relative contribution of each data to sea fog dissipation by means of calculating the feature importance of input data to DNN algorithm.
* Corresponding Author : Research Scientist, Department of Oceanographic Forecast, KHOA (Korea Hydrographic and Oceanographic Agency), 351, Haeyang-ro, Yeongdo-gu, Busan, Republic of Korea, zip code 49111, E-mail : firstname.lastname@example.org