Artificial Intelligence Forecasting of Marine Heatwaves in the South China Sea Using a Combined U-Net and ConvLSTM System

Abstract

Marine heatwaves (MHWs) are extreme events characterized by abnormally high sea surface temperatures, and they have significant impacts on marine ecosystems and human society. The rapid and accurate forecasting of MHWs is crucial for preventing and responding to the impacts they can lead to. However, the research on relevant forecasting methods is limited, and a dedicated forecasting system specifically tailored for the South China Sea (SCS) region has yet to be reported. This study proposes a novel forecasting system utilizing U-Net and ConvLSTM models to predict MHWs in the SCS. Specifically, the U-Net model is used to forecast the intensity of MHWs, while the ConvLSTM model is employed to predict the probability of their occurrence. The indication of an MHW relies on both the intensity forecasted by the U-Net model exceeding threshold T and the occurrence probability predicted by the ConvLSTM model surpassing threshold P. Incorporating sensitivity analysis, optimal thresholds for T are determined as 0.9 °C, 0.8 °C, 1.0 °C, and 1.0 °C for 1-, 3-, 5-, and 7-day forecast lead times, respectively. Similarly, optimal thresholds for P are identified as 0.29, 0.30, 0.20, and 0.28. Employing these thresholds yields the highest forecast accuracy rates of 0.92, 0.89, 0.88, and 0.87 for the corresponding forecast lead times. This innovative approach gives better predictions of MHWs in the SCS, providing invaluable reference information for marine management authorities to make well-informed decisions and issue timely MHW warnings.

Historical Marine Cold Spells in the South China Sea: Characteristics and Trends

“…The SST data for the SCS were extracted from this comprehensive dataset, spanning a duration of 14,975 days (from 1 January 1982 to 31 December 2022). This dataset has been extensively utilized in research related to phenomena such as MHWs, consistently validating its effectiveness [34,54,62,63].…”

“…The South China Sea (SCS), covering approximately 3.5 million square kilometers, stands as one of the deepest and largest semi-enclosed marginal seas in the northwestern Pacific Ocean (99-125 • E, 0-25 • N, Figure 1). Renowned for its rich coral reefs and abundant fishery resources [31][32][33][34], the SCS boasts a dynamic environment characterized by mesoscale eddies, internal waves, and various active processes, contributing to its complexity and diversity [35][36][37][38][39][40][41][42]. Bordered by landmasses to the north, west, and south, the SCS is delineated from the Pacific Ocean by the Philippine Islands and Taiwan Island to the east.…”

Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models

“…Quimbayo-Duarte et al [18] assessed the impact of forest parameterization on boundary layer flow simulations over moderately complex terrain using WRF-LES. In recent years, artificial intelligence technology has made significant progress, and LSTM neural networks have especially been successfully applied in various fields [19,20]. Using the LSTM neural network for wind power prediction not only saves time, but also improves the accuracy of prediction [21].…”

Comparisons of Different Machine Learning-Based Rainfall–Runoff Simulations under Changing Environments

“…The BP model has strong nonlinear mapping capabilities but suffers from complex structures, susceptibility to local optima during training, and difficulties in hyperparameter optimization. The LSTM model, with its gated unit structure, has a significant advantage in time series prediction with long-range dependencies but has drawbacks such as many internal parameters [21][22][23], slow training convergence, and high computational resource demands [24]. The CatBoost model efficiently handles the category typing features of the RR process, addresses gradient bias and prediction shift issues, reduces the occurrence of overfitting, and improves algorithm accuracy and generalization capability [25].…”