[1]张丽,吴金亮,杨国范.基于BP神经网络的东港灌区作物需水量预测研究[J].水土保持研究,2012,19(06):207-210,216.
　ZHANG Li,WU Jin-liang,YANG Guo-fan.Prediction Research for Crop Water Requirement Based on BP Neural Network in Donggang Irrigation District[J].Research of Soil and Water Conservation,2012,19(06):207-210,216.
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基于BP神经网络的东港灌区作物需水量预测研究

《水土保持研究》[ISSN:1005-3409/CN:61-1272/P] 卷: 19 期数: 2012年06期 页码: 207-210,216 栏目: 出版日期: 2012-12-20
Title:
Prediction Research for Crop Water Requirement Based on BP Neural Network in Donggang Irrigation District
作者:
张丽1,2, 吴金亮2, 杨国范2
1. 吉林师范大学旅游与地理科学学院, 吉林 四平 136000;
2. 沈阳农业大学水利学院, 沈阳 110866
Author(s):
ZHANG Li1,2, WU Jin-liang2, YANG Guo-fan2
1. College of Tourism and Geographical Science, Jilin Normal University, Siping, Jilin 136000, China;
2. College of Hydraulic Engineering, Shenyang Agricultural University, Shenyang 110866, China
关键词:
参考作物蒸散量; Matlab; BP神经网络
Keywords:
reference crop evapotranspiration; Matlab; BP neural networks
分类号:
S274.4
摘要:
为了指导东港地区的农业生产,对影响作物参考蒸散量(ET0)的气象因子进行了分析,利用东港市1999年和2000年水稻生育期的常规气象资料,采用Matlab软件的神经网络工具箱,建立ET0三层BP神经网络预测模型,网络的输入因子为日净辐射量、日平均相对湿度和日平均风速,输出因子为利用Penman-Monteith公式计算得到的同期ET0值.结果表明:(1) 当模型的隐层节点数为11,传递函数采用tansig函数,训练函数采用trainlm函数时,预测值与目标值的平均相对误差为9%,预测效果较其他情况都好,由此确定模型的最终结构为3-11-1的BP-ET0模型.(2) 用BP-ET0模型预测参考作物蒸散量,预测值与目标值的变化趋势基本一致.
Abstract:
In order to guide agricultural production in Donggang region, the impact of different meteorological factors on reference crop evapotranspiration (ET0) were analyzed. Using the neural network toolbox of Matlab software, a three-layer artificial neural network model was established to predict ET0 according to the conventional meteorological data during maize growth and development period of year 1999 and 2000 in Donggang city. In this network, the input variables were daily net radiation, daily average relative humidity and daily average wind speed, and the output variable was ET0 which was calculated by the Penman-Monteith formula. The results showed that: (1) the average relative error between prediction ET0 values and target ET0 values was 9%, when 11 neural nodes in the hidden layer was adopted, tansig function was chosen as transfer function and trainlm function was used to train the network, therefore, a 3-11-1 optimal network was determined; (2) the change tendency of the predicted values with BP-ET0 model prediction and the target was the basic consistency.

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备注/Memo

收稿日期:2012-5-15;改回日期:2012-7-24。
作者简介:张丽(1976- ),女(满族),辽宁兴城人,讲师,博士研究生,研究方向:遥感技术与地理信息系统应用.E-mail:zli_76@163.com

更新日期/Last Update: 1900-01-01