Classification and Prediction of Coral Reef Bleaching Severity through Machine Learning

Chuhan Feng ()
Additional contact information
Chuhan Feng: Macau University of Science and Technology

A chapter in Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025), 2026, pp 302-311 from Springer

Abstract: 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.

Keywords: Coral Reef Bleaching Prediction; Severity Classification; Random Forest; XGBoost; Neural Network (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-2-38476-585-0_36

Ordering information: This item can be ordered from
http://www.springer.com/9782384765850

DOI: 10.2991/978-2-38476-585-0_36

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().