World Data Ocean/Korean Rockfish (Sebastes schlegelii)

Korean Rockfish (Sebastes schlegelii)

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Multi-horizon forecasting of coastal high water temperature events for operational marine-heatwave early warning in South Korean aquaculture: a walk-forward benchmark of eleven statistical and deep-learning models
Frontiers in Marine Science | New and Recent Articles

Multi-horizon forecasting of coastal high water temperature events for operational marine-heatwave early warning in South Korean aquaculture: a walk-forward benchmark of eleven statistical and deep-learning models

Coastal aquaculture in South Korea faces repeated losses due to marine-heatwave events impacting key species like olive flounder. Addressing this, a recent study benchmarks eleven statistical and deep-learning models for forecasting high water temperatures, culminating in the development of the Hybrid GNN-BiLSTM. This innovative architecture demonstrates superior long-horizon predictive skill, achieving the lowest root-mean-square error and calibrated probabilistic forecasts. The research highlights a 38-hour advisory lead time, demonstrating the potential for operationally actionable early warnings—a critical advancement for protecting vulnerable aquaculture operations.