Hybrid U-Net and ConvLSTM-based spatial forecasting of marine heatwave intensity and occurrence probability
TL;DR
Overall, the hybrid deep learning framework demonstrated robust and stable performance in representing both the intensity and probability of MHW events up to a seven-day forecasting horizon.
Abstract
Marine Heatwaves (MHW) are extreme sea surface temperature events that significantly affect marine ecosystems, fisheries, and coral reef environments, highlighting the need for accurate forecasting systems in vulnerable regions such as the Lesser Sunda Islands. This study aims to develop a hybrid deep learning-based MHW prediction system by integrating U-Net and ConvLSTM models over the regions of Bali, West Nusa Tenggara, and East Nusa Tenggara. The datasets used consist of NOAA OISST V2.1 daily sea surface temperature data for the period 1985–2024, along with Niño 3.4 and Dipole Mode Index (DMI) atmospheric indices. The U-Net model was applied to predict Sea Surface Temperature Anomaly (SSTA) intensity, while ConvLSTM was used to estimate the probability of MHW occurrence. The results demonstrate that the best forecasting accuracies of the U-Net intensity model for 1-, 3-, 5-, and 7-day lead times reached 0.8896, 0.8597, 0.8462, and 0.8674, respectively, with optimal thresholds of 0.92 °C, 0.56 °C, 0.51 °C, and 0.31 °C. The ConvLSTM probability model produced maximum FAR values of 0.9101, 0.8962, 0.8941, and 0.8854 with optimal probability thresholds of 0.25, 0.1, 0.1, and 0.1. RMSE evaluation increased gradually from 0.27 °C on day-1 to 0.94 °C on day-7 forecasts. Overall, the hybrid deep learning framework demonstrated robust and stable performance in representing both the intensity and probability of MHW events up to a seven-day forecasting horizon.