Applying deep learning to predict SST variation and tropical cyclone patterns that influence coral bleaching

South Penghu Marine National Park, Taiwan experienced severe coral bleaching in 2020 that may lead to coral mortality. Corals are important animals that enrich marine biodiversity, but several studies have shown that the range of sea surface temperature (SST) corals can survive is narrow, and recovery time can be as long as 10 years. The influences of climate change induced extreme weather should not be underestimated, and it is critical to accurately predict SST variations. In this study, a deep learning model, convolutional long short-term memory (ConvLSTM), which is a newer model suitable for estimating autocorrelated sequences, was built to predict daily SST for the next day, next week, and next two weeks, with the highest achieved 99.22% accuracy and a RMSE values of 0.2579. On the other hand, Penghu was not directly impacted by tropical cyclones in 2020; hence the lack of typhoon-induced SST cooling could be one of the reasons for extensive coral bleaching. The wind intensity, speed, as well as the thickness of ocean mixed layer all affect the variation of SST cooling. The results show that the degree heating weeks (DHWs) near Penghu achieved its highest level in 2020, and according to the analysis of the study, there is a clear association between climate change and coral bleaching risk increase in recent years. Therefore, we applied Random Forest to establish a machine learning model to classify whether a typhoon can trigger significant cooling. Model accuracy achieved 92.90% in the testing set, and empirical orthogonal function (EOF) analysis contributed the most to the SST cooling pattern among typhoons. This study provides a predictive contribution to global coral conservation for relevant institutions to know the risk beforehand, and they can discuss corresponding strategies and early warnings.