| 基于随机样本的神经网络模型估算参考作物腾发量 |
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| 引用本文： | 高瑞忠,朝伦巴根,于婵,朱仲元,柴建华.基于随机样本的神经网络模型估算参考作物腾发量[J].农业工程学报,2006,22(2):42-45. |
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| 作者姓名： | 高瑞忠 朝伦巴根 于婵 朱仲元 柴建华 |
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| 作者单位： | 1. 内蒙古农业大学水利与土木工程建筑学院,呼和浩特,010018
2. 内蒙古水利科学研究院,呼和浩特,010020 |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | 参考作物腾发量(ET0)是计算作物需水量、制定灌溉制度和进行水资源管理的主要参数之一。计算参考作物腾发量(ET0)的方法众多，为规范ET0的求法，联合国粮农组织(FAO)推荐采用修改的Penman-Monteith方法。该文指出不需要收集长序列气象资料，而以随机样本建立学习速率和动量因子自适应的BP神经网络模型估算参考作物腾发量(ET0)的方法，并且与FAO推荐的Penman-Monteith法计算值对比分析，结果表明：利用随机样本建立的的BP神经网络模型可以很好的反映气象因子(最高温度、最低温度、最大湿度、最小湿度、净辐射和风速)与参考作物腾发量(ET0)的非线性函数映射关系，并且取得了良好的估算效果，给出了国家自然科学基金重点项目研究区内蓝旗试验站2004年的时间尺度为日、十日参考作物腾发量(ET0)的计算及对比分析过程。
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| 关 键 词： | 随机样本 BP神经网络模型 估算 参考作物腾发量 |
| 文章编号： | 1002-6819(2006)02-0042-04 |
| 收稿时间： | 3/8/2005 12:00:00 AM |
| 修稿时间： | 2005-11-21 |
Estimating referencecrop evapotranspiration using artificial neural network based on random samples |
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| Gao Ruizhong,Chaolunbagen,Yu Chan,Zhu Zhongyuan and Chai Jianhua.Estimating referencecrop evapotranspiration using artificial neural network based on random samples[J].Transactions of the Chinese Society of Agricultural Engineering,2006,22(2):42-45. |
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| Authors: | Gao Ruizhong Chaolunbagen Yu Chan Zhu Zhongyuan and Chai Jianhua |
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| Institution: | 1. College of Hydraulic and Civil Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China; 2. Institute of Inner Mongolia Water Resources, Huhhot 010020, China |
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| Abstract: | Quantification of referencecrop evapotranspiration(ET0) is necessary in the context of many issues, for example calculating crop requirement, scheduling of irrigation and the management of water resource so on. Random samples instead of the long-term climatic data were used as samples to construct Artificial Neural Network(ANN) model for estimating ET0 and the performance of ANN was compared with the method of modified Penman-Monteith. The maximum and minimum temperature, maximum and minimum relative humidity, solar radiation, wind speed were selected as network inputs and ET0 was selected as network output. After calculating, the result shows that the estimation of ANN has higher accuracy by error analysis. |
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| Keywords: | random samples artificial neutral network estimation referencecrop evapo transpiration |
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