BP人工神经网络模拟杨树林冠蒸腾  

Modeling canopy transpiration of young poplar trees( Populus × euramericana cv. N3016) based on Back Propagation Artificial Neural Network

作　　者：李辉东[1,2] 关德新[1] 袁凤辉[1] 王安志[1] 吴家兵[1] 金昌杰[1] 

机构地区：[1]森林与土壤生态国家重点实验室,中国科学院沈阳应用生态研究所,沈阳110016 [2]中国科学院大学,北京100049

出　　处：《生态学报》2015年第12期4137-4145,共9页Acta Ecologica Sinica

基　　金：国家“十二五”科技支撑计划项目(2011BAD38B0203)

摘　　要：利用2008和2010年的气温、饱和差、总辐射和叶面积指数作为模型输入,液流法观测的蒸腾速率作为模型输出,建立了用于杨树林冠蒸腾模拟的BP人工神经网络模型,利用2009年的观测数据对模型的模拟能力进行了检验,并应用连接权值计算得到的输入变量对输出变量的相对贡献进行了敏感性分析。结果表明:建立的BP人工神经网络蒸腾模型可以很好的模拟林冠蒸腾大小和季节变化,模拟的绝对误差和绝对相对误差的平均值分别为0.11 mm/d和9.5%,纳什效率系数为0.83;输入变量对蒸腾的相对贡献以及蒸腾与输入变量之间的相关性大小顺序相同,均为总辐射>叶面积指数>饱和差>气温。Artificial neural network （ANN） is a practical tool and a powerful alternative to mechanism models in operation of hydrology modeling. In this paper, a three layer back propagation （BP） artificial neural network model was developed to estimate the canopy transpiration of young poplar trees （Populus × euramericana cv. N3016） in Northeast China. The combination of air temperature （Ta）, vapor pressure deficit （VPD）, solar radiation （Rg） and leaf area index （LAI） was chosen as the input variables, while the transpiration measured by sap flow was chosen as output variable. Observational data in growing season of 2008 and 2010 was used to develop model. The number of neurons in the input layer and output layer was 4 and 1, respectively based on the number of input and output variables. Levenberg-Marquardt （LM） algorithm was selected as the learning algorithm to train the network. Tansig and Logsig function were selected as the transfer function in the hidden layer and output layer, respectively. The learning rate and momentum factor were set as 0.1 and 0.01, respectively. The number of neurons in the hidden layer was optimized as 9 by a trial and error method. So the network structure of the developed model was determined as 4：9：1. After 49 times training, the optimal BP ANN transpiration model was determined. The data samples in 2009 were chosen to evaluate the developed model. Results showed that BP ANN transpiration model can successfully simulate the seasonal variation of transpiration. The slope of the regression equation between the simulated and measured transpiration was 0.99, while R2 was 0.85. Maximum and minimum absolute error were 0.28 mm/d and 0.003 mm/d. Mean absolute error and mean absolute relative error were 0.11 mm/d and 9.5%, and Nash-Sutcliffe coefficient of efficiency were 0.83, which all indicated the high accuracy and efficiency of developed BP ANN model. However, compared with the model performance during training process, the accuracy decreased slightly, whi

关 键 词：蒸腾模拟 BP神经网络 液流法 敏感性分析 

分 类 号：S792.11[农业科学—林木遗传育种] TP183[农业科学—林学]