Affiliation:
1. Roger F. Wicker Center for Ocean Enterprise, The University of Southern Mississippi, Gulfport, MS 39501, USA
Abstract
While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noise, typically after an extreme event occurrence or some accidental damage to the sensors. Such data artifacts, if not handled diligently prior to modeling, can significantly impact the reliability of any further predictive analysis. Therefore, we present a framework that integrates data reconstruction of key sea state variables and multi-step-ahead forecasting of current speed from the reconstructed time series for 19 depth levels simultaneously. Using multivariate chained regressions, the reconstruction algorithm rigorously tests from an ensemble of tree-based models (fed only with surface characteristics) to impute gaps in the vertical profiles of the sea state variables down to 20 m deep. Subsequently, a deep encoder–decoder model, comprising multi-head convolutional networks, extracts high-level features from each depth level’s multivariate (reconstructed) input and feeds them to a deep long short-term memory network for 24 h ahead forecasts of current speed profiles. In this work, we utilized Viking buoy data, and demonstrated that with limited training data, we could explain an overall 80% variation in the current speed profiles across the forecast period and the depth levels.
Funder
The Roger F. Wicker Center for Ocean Enterprise
The University of Southern Mississippi, USA
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
Reference68 articles.
1. Spatiotemporal neural network with attention mechanism for El Niño forecasts;Kim;Sci. Rep.,2022
2. A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder;Wang;Comput. Geosci.,2021
3. Successful blue economy examples with an emphasis on international perspectives;Wenhai;Front. Mar. Sci.,2019
4. Intelligent multi-AUG ocean data collection scheme in maritime wireless communication network;Wen;IEEE Trans. Netw. Sci. Eng.,2022
5. Kar, S., Sunkara, V., McKenna, J., Stanic, S., and Bernard, L. (2022). OCEANS 2022, Hampton Roads, IEEE.