| 基于天气预报的参考作物腾发量LS-SVM预测模型 |
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| 引用本文： | 张展羽,王声锋,段爱旺,王斌.基于天气预报的参考作物腾发量LS-SVM预测模型[J].水科学进展,2010,21(1):63-68. |
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| 作者姓名： | 张展羽 王声锋 段爱旺 王斌 |
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| 作者单位： | 1.河海大学南方地区高效灌排与农业水土环境教育部重点实验室, 江苏, 南京, 210098; |
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| 基金项目： | 公益性行业科研专项经费资助项目，国家自然科学基金资助项目 |
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| 摘 要： | 利用最小二乘支持向量机(LS-SVM)方法,建立了基于天气预报的参考作物腾发量(ET0)的预测模型.对广利灌区1997~2006年逐日气象信息中的天气类型和风速等级进行量化后,以不同天气预报信息作为输入量,建立10种验证方案,对2007年的逐日ET0进行预测.经验证,方案1~方案7精度均令人满意,其中方案1精度最高.方案1的输入量为气温、天气类型、风速等级3项的预测值,该方案的模型预测值与计算值的统计参数分别为:均方根偏差ERMS为0.5182 mm,相对偏差ER为0.1878,决定系数R2为0.864 8,认同系数IA为0.966 9,回归系数RC为0.9867;方案7精度亦较好,且以上指标统计参数依次为0.6576 mm、0.2332、0.986 6、0.774 7及0.986 6,该方案输入量只有气温项,实用性很强.
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| 关 键 词： | 天气预报 参考作物腾发量 最小二乘支持向量机 预测模型 |
| 收稿时间： | 2009-02-28 |
Least squares support vector machines model for predicting reference evapotranspiration based on weather forecasts |
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| ZHANG Zhan-yu,WANG Sheng-feng,DUAN Ai-wang,WANG bin.Least squares support vector machines model for predicting reference evapotranspiration based on weather forecasts[J].Advances in Water Science,2010,21(1):63-68. |
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| Authors: | ZHANG Zhan-yu WANG Sheng-feng DUAN Ai-wang WANG bin |
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| Institution: | 1.Key Laboratory of Efficient Irrigation-Drainage and Agriculture Soil-Water Environment in Southern China, Ministry of Education, Hohai University, Nanjing 210098, China;2.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;3.North China University of Water Resources and Electric Power, Zhengzhou 450011, China;4.Farmland Irrigation Research Institute, CAAS, Xinxiang 453003, China |
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| Abstract: | A reference evapotranspiration(ET0) prediction model is developed based on the least squares support vector machines.Weather forecasts are used for ET0 predictions.The model can be trained with daily weather parameters including quantified weather types and wind grades,etc.Different combinations of daily weather parameters can be tested in the model training processes.In this study,the daily weather parameters are obtained from the Guangli irrigation district during the period 1997-2007.The 1997-2006 data a... |
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| Keywords: | weather forecasts reference evapotranspiration least squares support vector machines prediction mode |
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