Exploration on Environmental Causes of Coral Bleaching

Qinyu Zheng

doi:10.62177/jaet.v1i3.84

Authors

Qinyu Zheng Culver Academies

DOI:

https://doi.org/10.62177/jaet.v1i3.84

Keywords:

Deep Learning, Stacking Regressor Model, SVR, LSTM, Coral Bleaching Prediction

Abstract

In recent years, since global warming and human activities have contributed to massive coral bleaching events, it is significant to seek for the causations and predict the rate of coral bleaching to mitigate the influence and to decelerate bleachi,ng rate. The study focused on analyzing coral bleaching database from 1980 to 2020, revealing sea surface temperature anomaly (SSTA) and temperature cumulative thermal stress (TSA_DHW) are the major contributor of corals bleaching. In addition, climatic factors such as wind speed and cyclone frequency also conduce to coral bleaching. Resulted from principial component analysis (PCA), random forest regressor, dominant influencing factors are utilized in training multi-layer long short-term memory RNN (LSTM), support vector regression (SVR), and stacking regressor model, establishing models that predict coral bleaching percentage. Eventually, mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) are used to evaluate the accuracy of the model, revealing stacking regression model yielded the most accurate and steady predictions on coral bleaching percent comparing with other models.

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