Abstract
Accurate and timely information of surface currents is crucial for various operations such as search and rescue, marine renewable energy extraction and oil spill treatment. Conventional approaches to study coastal surface currents are numerical models and observation platforms such as radars and satellites. However, both have limits. To efficiently obtain high accuracy short-term forecasting states of oceanic parameters of interest, a robust soft computing approach—Artificial Neural Networks (ANN)—was applied to predict surface currents in a tide- and wind-dominated coastal area. Hourly observed surface currents from a Coastal Ocean Dynamic Application Radar (CODAR) system, and tide and wind data from forecasting models were used to establish ANN models for Galway Bay area. One of the fastest algorithms, resilient back propagation, was used to adapt all weights and biases. This study focused on investigating the sensitivity of an ANN model to a series of different input datasets. Results indicate that correlation between ANN forecasts and observation was greater than 0.9 for both surface velocity components with one-hour lead time. Strong correlation ( ≥ 0.75) was obtained between predicted results and radar data for both surface velocity components with three-hour lead time at best. However, forecasting accuracy deteriorated rapidly with longer lead time. By comparison with previous data assimilation models, in this research, best performance was achieved from ANN model’s peak times of the tidally dominant surface velocity component. The forecasts presented in this research show clear improvements over previous attempts at short-term forecasting of wind- and tide-dominated currents using ANN.
Highlights
Interactions between processes such as wind, surface water and tide are dominant factors driving the movement of water in coastal regions
Tlhoecarteiosnilsie. nTthbeactrkaipnrionpgadgataiosnet awlgaosritthhemn musoeddifitoesepstaarbalmisheteArsNoNf amnoeduerlas.l Tnhetewtoesrktintgo fidnatdasaetlowcaals mapinpilmieudmtooefxtahme ienreroarnfdunascsteiosns r[e5s2u].lts from the newly developed Artificial Neural Networks (ANN) models, the best training ANN model was determined based on the model generating the minimum Room-Mean-Square-Error (RMSE) between radar observations and ANN model forecasts
The testing dataset was applied to examine and assess results from the newly developed ANN models, the best training ANN model was determined based on the model generating the minimum Room-Mean-Square-Error (RMSE) between radar observations and ANN model forecasts
Summary
Interactions between processes such as wind, surface water and tide are dominant factors driving the movement of water in coastal regions. Numerical models and observation platforms based on remote sensing technologies are conventional tools to study characteristics of coastal surface currents and provide useful information. Numerical models that mathematically describe dynamic processes are often used to produce forecasts of surface currents. Difficulties in the definition of initial and boundary conditions, grid structure on horizontal and vertical planes and simplification of parameters inevitably result in model errors that may be significant. Oceanic observation tools such as satellites, radars and Acoustic Doppler Current Profiles (ADCP) are powerful means of monitoring near real-time oceanic currents over large spatial domains; these tools cannot implicitly provide forecasting states of surface currents
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