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Інформація × Реєстраційний номер 2123U006663, Матеріали видань та локальних репозитаріїв Категорія Препринт Назва роботи Ocean surface visibility prediction Автор Prypeshniuk Volodymyr Дата публікації 01-01-2023 Постачальник інформації Український католицький університет Першоджерело https://hdl.handle.net/20.500.14570/3948 Видання Опис Seawater transparency is an indispensable ecological parameter with substantial impacts on the health and productivity of aquatic ecosystems. Its significance spans across various industries, including environment protection, fishing and tourism. The fluctuating nature of aquatic systems and their intricate interplay with human activities often induce substantial variability in seawater transparency. This underlines the pressing necessity for effective predictive tools in the stewardship and preservation of our invaluable water resources. Despite the clear importance of water transparency, ocean forecasting remains a considerably understudied field, some work has been done on using satellite for monitoring, but literature is scarce for forecasting with only few simple models explored. There is an evident gap in research and tools focused on predicting changes in this crucial ecosystem, underlining the novelty and urgency of our work. In this research, we aim is to develop a forecasting model that not only excels in precision and speed, but is also flexible enough to encompass a vast array of potential future scenarios. We primarily employed SimVP, a spatio-temporal convolutional neural network, for ocean forecasting purposes. This model was trained using the earth observation data from the Copernicus Marine Service. This data were collected for 20 years of daily observation of water transparency in the marine environment surrounding the UK, with a spatial resolution of 4km x 4km. Our findings showed that SimVP substantially outperformed the baseline models (AutoRegressive Integrated Moving Average (ARIMA) and Simple Exponential Smoothing (SES)) in predicting the next day seawater transparency, demonstrating an improvement of 17.4%, and a notable reduction in the Root Mean Square Error (RMSE) from 2.63 to 2.24, and improvement in inference time efficiency in 66.3 times (334.6 -> 5.04 seconds). We show that this method better performs better on regions with minor variation like Irish Sea or English Channel, and performs worse on regions with high variations like Atlantic Ocean or North Sea. Our study demonstrates the advantage of adopting the spatio-temporal neural network architectures for ocean monitoring and paves the way for future research in adopting advanced machine learning techniques in this field. Додано в НРАТ 2025-05-09 Закрити