| 参考作物腾发量主成分神经网络预测模型 |
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| 引用本文： | 彭世彰,魏 征,徐俊增,丁加丽.参考作物腾发量主成分神经网络预测模型[J].农业工程学报,2008,24(9):161-164. |
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| 作者姓名： | 彭世彰 魏 征 徐俊增 丁加丽 |
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| 作者单位： | 1. 河海大学水文水资源与水利工程科学国家重点实验室,南京,210098;河海大学农业工程学院,南京,210098
2. 河海大学水文水资源与水利工程科学国家重点实验室,南京,210098 |
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| 基金项目： | 国家自然科学基金，国家科技支撑计划 |
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| 摘 要： | 为解决采用神经网络模型预测参考作物蒸发蒸腾最Eto研究中预测能力不足的问题,将气象因子包括最高、最低和日平均温度、日照时数、气压、水汽压、相对湿度和风速进行主成分分析,提取主成分,建立了基于主成分的三层BP神经网络模型.选取崇川水利科学试验站2001年到2004年的旬气象资料,采用Matlab神经网络工具箱进行模型训练与预测,并以传统BP网络模犁作为对照.结果表明,主成分网络模型能够很好地反映诸多影响因子与Eto之间的关系,尤其对训练样本以外的验证样本,主成分网络模型具有显著优于传统BP网络模型的识别能力,取得更为可靠的预测结果.
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| 关 键 词： | 主成分分析 神经网络 参考作物蒸发蒸腾量 预测能力 |
| 收稿时间： | 2007/7/14 0:00:00 |
| 修稿时间： | 2008/8/23 0:00:00 |
Estimation model for reference evapotranspiration by neural network based on principal components |
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| Peng Shizhang,Wei Zheng,Xu Junzeng and Ding Jiali.Estimation model for reference evapotranspiration by neural network based on principal components[J].Transactions of the Chinese Society of Agricultural Engineering,2008,24(9):161-164. |
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| Authors: | Peng Shizhang Wei Zheng Xu Junzeng and Ding Jiali |
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| Institution: | 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; 2. College of Agricultural Engineering, Hohai University, Nanjing 210098, China,1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; 2. College of Agricultural Engineering, Hohai University, Nanjing 210098, China,1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; 2. College of Agricultural Engineering, Hohai University, Nanjing 210098, China and State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China |
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| Abstract: | In order to improve the performance of neural network model for the prediction of reference crop evapotranspiration, principal component analysis are applied to the weather data including the maximum, minimum and average daily temperature, sunshine duration, air pressure, humidity of exposure field, air relative humidity and wind velocity, and a three-layer BP(back-propagation) neural network model is constructed based on the principal components. Based on ten-day average weather data from 2001 to 2004 in the Water Conservancy Science Experimental Station in Chongchuan, the principal-component-based model was trained and predicted with Matlab neural network toolbox, and compared with tranditional BP neural network. Results show that the principal-component-based BP network model can well reflect the relationship between environmental factors and ET0, and is superior to the general BP network model in the prediction of ET0, especially for the validation samples outsides training dataset, which shows better performance of the principal component BP network model in comparison with the traditional BP network model. |
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| Keywords: | principal component analysis neural network reference evapotranspiration prediction ability |
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