Classification and Prediction of Coral Reef Bleaching Severity through Machine Learning

DOI

10.2991/978-2-38476-585-0_36How to use a DOI?

Keywords

Coral Reef Bleaching Prediction; Severity Classification; Random Forest; XGBoost; Neural Network

Abstract

This study developed a machine learning framework that uses environmental variables to predict the degree of coral reef bleaching, filling some important gaps in current predictions. Apply random forest models, XGBoost, and neural network models in projects involving variant forests and pH anomalies, as well as temporal data to capture complex ecological interactions. The system sample corrected the widespread imbalance in coral data. A comparative analysis shows that the Random Forest model achieved an accuracy of 80% with a micro-average Area Under the Receiver Operating Characteristic Curve (ROC) Curve of 0.9118. This research goes beyond the binary classification method, which allows for severity measurement and identifies significant environmental factors by analyzing resources. The research results have created a powerful data-intensive framework for coral conservation measures under climate pressure. Future work will combine satellite observations and computer visualization to achieve a more precise resolution, ultimately enabling more proactive and targeted intervention strategies for preserving vulnerable reef ecosystems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Chuhan Feng
PY  - 2026
DA  - 2026/06/18
TI  - Classification and Prediction of Coral Reef Bleaching Severity through Machine Learning
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 302
EP  - 311
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_36
DO  - 10.2991/978-2-38476-585-0_36
ID  - Feng2026
ER  -