ArticleEcology and evolution2025
Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models.
Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Spatiotemporal interaction and attribution analysis of ecosystem health and human activities in the Daxing'anling forest-grassland Ecotone, China.Frontiers in plant science · 2026Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
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 southern China, focusing on different types of subtropical forests. This region harbors several types of subtropical forests, which are rarely found at similar latitudes in the world. Variance inflation factor was employed to screen independent variables, resulting in the selection of 13 significant predictors. Four machine learning models-support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and extreme gradient boosting (XGB)-were constructed to estimate carbon storage. Model performance was evaluated using root mean square error, coefficient of determination (
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