CAJ | 학술논문

如何根据影响因素较好地预测碳通量是许多环境监测者非常关注的问题.但至今尚无一种非常有效的预测模型,为此研究ε-支持向量回归机在碳通量预测中的具体应用,并与BP神经网络模型的预测结果做了比较,分析了两种方法在核函数及相关参数、网络结构、神经元数目选择方面各自不同的特点.实验结果表明,基于ε-支持向量回归机和BP神经网络模型的碳通量预测结果与碳通量实测值之间存在显著相关性.但ε-支持向量回归机方法的预测过程更易掌控,整体预测精度高于BP神经网络的精度.
여하근거영향인소교호지예측탄통량시허다배경감측자비상관주적문제.단지금상무일충비상유효적예측모형,위차연구ε-지지향량회귀궤재탄통량예측중적구체응용,병여BP신경망락모형적예측결과주료비교,분석료량충방법재핵함수급상관삼수、망락결구、신경원수목선택방면각자불동적특점.실험결과표명,기우ε-지지향량회귀궤화BP신경망락모형적탄통량예측결과여탄통량실측치지간존재현저상관성.단ε-지지향량회귀궤방법적예측과정경역장공,정체예측정도고우BP신경망락적정도.
Precisely predicting the carbon flux through impact factors has auracted many ecologists' interest.However,there is still no perfect method to predict carbon flux effectively.In this paper,ε-support vector regression(ε-SVR) is used to predict carbon flux,and the results of ε-SVR and BP neural network(BPNN) for the prediction of carbon flux are compared.ε-SVR with differ-ent kernel functions and parameters and BPNN with different numbers of the neurons in hidden layer are analyzod.The experi-ment results show that the correlation between the carbon flux predicted by ε-SVR and BPNN and the observation values is high,However,ε-SVR converges global optimal more easily than BPNN.And the ε-SVR predicts more accurately than BPNN.