Marine Chlorophyll Prediction Based on Gray Correlation Analysis and Random Forest Modeling

Xiaoyi Liu

doi:10.54097/x2hpgs53

Authors

Xiaoyi Liu

DOI:

https://doi.org/10.54097/x2hpgs53

Keywords:

Marine Chlorophyll, Grey Correlation Analysis, Random Forest Modeling.

Abstract

This study investigates the relationship between marine chlorophyll content and environmental factors using ocean observation big data. Through data preprocessing, gray correlation analysis, Spearman correlation analysis, and random forest modeling, the research identifies key environmental drivers of chlorophyll content, including carbon dioxide levels, seawater temperature, salinity, pH, and dissolved oxygen. The results reveal significant negative correlations between carbon dioxide and temperature with chlorophyll content, underscoring the potential role of marine chlorophyll in carbon sequestration. This work enhances understanding of marine ecosystem dynamics and provides a scientific basis for marine resource management and ecological conservation. By integrating multiple analytical methods, the study offers a novel approach for predicting marine chlorophyll content, contributing to global carbon cycle research and supporting sustainable marine resource management under environmental change.

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