黄土高原油松人工林水文效应的人工神经网络模型  

Artificial Neural Network Model of the Hydrological Effects of the Pinus tabulaeformis Plantations in Loess Plateau

作　　者：赵明扬[1] 孙长忠[1] 康磊[1] 

机构地区：[1]中国林业科学研究院华北林业实验中心,北京102300

出　　处：《西南林业大学学报（自然科学）》2013年第3期52-55,86,共5页Journal of Southwest Forestry University：Natural Sciences

基　　金：国家自然科学基金项目(30170769)资助;华北林业实验中心科学试验示范林功能恢复与提升研究项目(CAFYBB2012004)资助

摘　　要：在具有半干旱黄土区典型地貌与气候特征的山西省偏关县,以油松人工林为研究对象,根据"林分自创性"假说的水量平衡关系式各变量及相互关系,结合试验样地林分情况确定神经网络的输入变量和输出变量,构建了5∶q∶1的BP神经网络模型。利用2008—2011年的567组数据对网络模型进行训练和检验,得到最适宜的网络结构为5∶6∶1,均方误差函数为mse=0.002 888,总体拟合精度为93.87%,模拟检验拟合精度为93.35%。Taking the Pinus tabulaeformis plantation in Pianguan County, Shanxi Province, with typical semi- arid loess landform and climate characteristics, as the research object, the 5：q： 1 BP artificial neural network model was established based on the correlations of the variables in the water balance formula by the hypothesis of ＇ brought by the stand itself＇ , integrating with the actual local conditions of the experimental plots to determine the input and output variables. The neural network model was testified with 567 groups of data observed from 2008 to 2011, and the optimum network structure was obtained as 5 ： 6 ： 1. The results showed that the mean square error was 0. 002 888, with the general fitting accuracy as 93.87%, and the testified fitting accuracy was 93.35%.

关 键 词：林分自创性 水量平衡 人工神经网络 油松 黄土高原 

分 类 号：S715.7[农业科学—林学]