森林工程2026，Vol.42Issue(4)：681-693,13.DOI:10.7525/j.issn.1006-8023.2026.04.003
基于知识引导型机器学习模型的黑龙江省天然林固碳潜力预测
Prediction of Carbon Sequestration Potential in Heilongjiang Province's Natural Forests Based on the KGML Model
摘要
Abstract
Accurate quantification of carbon stocks and their dynamic changes in forest ecosystems is essential for assess-ing regional carbon sink potential and addressing climate change.However,conventional machine learning models often suffer from reduced extrapolation ability in long-term predictions due to the absence of ecological process constraints.To overcome this limitation,this study established a knowledge-guided machine learning(KGML)framework for natural for-ests in Heilongjiang Province by integrating fixed-plot observations from 1976-2015 with historical and future climate and CO₂ concentration datasets.The CO₂ fertilization effect was incorporated and optimized into the forest carbon seques-tration(FCS)model,and its simulated results were used as prior features input into forest carbon sequestration model(XGBoost)to achieve the fusion of process knowledge with data-driven learning.Results showed that the optimized FCS model significantly improved prediction accuracy(R² increased from 0.85 to above 0.90),while KGML model demon-strated superior simulation accuracy of carbon density acrossvarious forest types compared to machine learning and pro-cess models,while also exhibiting greater stability and extrapolation capability in long-term predictions(≥10 years).Scenario simulations indicated that natural forests under SSP126 would reach the highest sequestration potential by 2060,with carbon stocks of 1 379.26-1 439.02 Tg C.This study demonstrates the effectiveness and potential for promo-tion of knowledge-guided machine learning in forest carbon prediction and provides a new path for improving the accu-racy of carbon sink assessment and achieving the dual-carbon goals.关键词
森林固碳潜力/极端梯度提升树模型/知识引导/森林固碳模型/长期预测/天然林/CO2施肥效应/气候变化情景Key words
Forest carbon sequestration potential/extreme gradrent hoosting model(XGBoost)/knowledge-guided/for-est carbon sequestration model(FCS)/long-term prediction/Natural forest/CO₂ fertilization effect/Climate change scenarios分类
农业科技引用本文复制引用
耿鲁堃,卞少杰,马新泰,付秋实,周源,王斌..基于知识引导型机器学习模型的黑龙江省天然林固碳潜力预测[J].森林工程,2026,42(4):681-693,13.基金项目
"十四五"国家重点研发计划课题项目(2023YFD2201704). （2023YFD2201704）
