Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (17): 263-265.

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Research for model of BP neural network in forecasting quantity of forest carbon fixation and oxygen release

REN Hong’e, JIAO Yuanyuan   

  1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
  • Online:2013-09-01 Published:2013-09-13



  1. 东北林业大学 信息与计算机工程学院,哈尔滨 150040

Abstract: In order to achieve the digital estimates of quantity of forest carbon fixation and oxygen release, a forest carbon fixation release oxygen prediction model based on BP neural network is proposed according to the shortage of existing estimation methods. In addition to studying neural network theory and carbon fixation oxygen release quantity estimation model, it analyzes forest CO2 flux trends in the growing season, the training samples are pretreated using normalization methods, BP neural network training is conducted, and a neural network model of CO2 flux is established combining relaxation eddy accumulation method and chamber method. Experimental results show that the model has good generalization performance and can more accurately estimate the quantity of carbon fixation and oxygen release of forest.

Key words: neural network, normalization methods, Back Propagation(BP) algorithm, carbon fixation model

摘要: 为了实现林木固碳释氧量的数字化估算,针对现有估算方法的不足,提出了基于BP神经网络的林木固碳释氧量的预测模型。基于对神经网络理论和固碳释氧量估算模型的研究,分析了林木在生长季节的CO2通量变化趋势,采用规范化方法对训练样本预处理,进行BP神经网络训练,并结合弛豫涡旋积累法和箱式法,建立了CO2通量神经网络模型。实验结果表明,所建模型具有较好的泛化性能,能够比较准确地估算出林木的固碳释氧量。

关键词: 神经网络, 规范化方法, 反向传播(BP)算法, 固碳模型