Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models

ABSTRACT Motivated by the need to enhance the accuracy of forest aboveground carbon storage (ACS) assessments, this study aimed to explore the effectiveness of different machine learning models in predicting ACS across various subtropical forest types in southern China. The study was conducted in so...

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Bibliographic Details
Main Authors:	Jiarun Liu, Zihang Yang, Lin Li, Xiaoxue Chu, Shiguang Wei, Juyu Lian
Format:	Article
Language:	English
Published:	Wiley 2025-05-01
Series:	Ecology and Evolution
Subjects:	aboveground carbon storage prediction factor contributions machine learning models multi‐layer perceptron random forest SHAP values
Online Access:	https://doi.org/10.1002/ece3.71499
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Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models

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