A Comparison of Machine Learning Algorithms for Predicting Sea Surface Temperature in the Great Barrier Reef Region

TL;DR

This study compares various machine learning algorithms for predicting sea surface temperature in the Great Barrier Reef, finding ensemble methods like XGBoost offer superior accuracy and distribution alignment, aiding environmental management.

Contribution

It provides a comprehensive comparison of ML techniques for SST prediction, highlighting the effectiveness of ensemble methods like XGBoost over traditional models.

Findings

XGBoost outperforms other models in predictive accuracy.

Ensemble methods significantly improve SST prediction.

XGBoost minimizes Kullback-Leibler Divergence effectively.

Abstract

Predicting Sea Surface Temperature (SST) in the Great Barrier Reef (GBR) region is crucial for the effective management of its fragile ecosystems. This study provides a rigorous comparative analysis of several machine learning techniques to identify the most effective method for SST prediction in this area. We evaluate the performance of ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, and Extreme Gradient Boosting (XGBoost) algorithms. Our results reveal that while LASSO and ridge regression perform well, Random Forest and XGBoost significantly outperform them in terms of predictive accuracy, as evidenced by lower Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Prediction Error (RMSPE). Additionally, XGBoost demonstrated superior performance in minimizing Kullback- Leibler Divergence (KLD), indicating a closer alignment of predicted probability distributions with actual observations. These findings highlight the efficacy of using ensemble methods, particularly XGBoost, for predicting sea surface temperatures, making them valuable tools for climatological and environmental modeling.