| 基于最小二乘向量机土壤水分动态模拟与分析 |
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| 引用本文： | 邓建强,陈效民,王伯仁,黄晶,杜臻杰,张勇.基于最小二乘向量机土壤水分动态模拟与分析[J].水土保持通报,2009,29(6):119-122. |
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| 作者姓名： | 邓建强 陈效民 王伯仁 黄晶 杜臻杰 张勇 |
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| 作者单位： | 邓建强,陈效民,杜臻杰,张勇,DENG Jian-qiang,CHEN Xiao-min,DU Zhen-jie,ZHANG Yong(南京农业大学,资源与环境科学学院,江苏,南京,210095);王伯仁,黄晶,WANG Bai-ren,HUANG Jing(中国农业科学院,祁阳农田生态国家野外试验站,湖南,祁阳,426182) |
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| 基金项目： | 国家重点基础(973计划)研究发展计划，国家重点实验室基金 |
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| 摘 要： | 土壤水分动态的模拟对水分循环与农业生产中水分的合理利用与管理具有重要的意义.应用最小二乘支持向量机对加入气象因子随机变量的红壤中土壤水分动态变化进行了训练、检验及模拟.结果表明,最小二乘支持向量机相比与神经网络方法不论是模拟性能指标还是建模的数学意义都有更好的可靠性和优越性;本研究应用最小二乘支持向量机对土壤水分动态日变化进行了模拟,并采用bior 3.3小波函数5层分解提取日变化趋势图进而把该研究区土壤水分日变化划分为4个阶段,其结果可为研究区水分合理利用和土壤墒情的预测预报提供科学依据.
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| 关 键 词： | 最小二乘向量机 土壤水分动态模拟 气象因子 小波 |
| 收稿时间： | 2009/4/16 0:00:00 |
| 修稿时间： | 2009/5/21 0:00:00 |
Simulation and Analysis of Soil Water Dynamic Change Based on Least Square Support Vector Machine |
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| DENG Jian-qiang,CHEN Xiao-min,WANG Bai-ren,HUANG Jing,DU Zhen-jie and ZHANG Yong.Simulation and Analysis of Soil Water Dynamic Change Based on Least Square Support Vector Machine[J].Bulletin of Soil and Water Conservation,2009,29(6):119-122. |
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| Authors: | DENG Jian-qiang CHEN Xiao-min WANG Bai-ren HUANG Jing DU Zhen-jie and ZHANG Yong |
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| Institution: | College of Resources and Environmental Sciences,Nanjing Agricultural University,Nanjing,Jiangsu 210095,China;College of Resources and Environmental Sciences,Nanjing Agricultural University,Nanjing,Jiangsu 210095,China;National Observation and Research Station of Farmland Ecosystem of Qiyang,Chinese Academy of Agricultural Sciences,Qiyang,Hunan 426182,China;National Observation and Research Station of Farmland Ecosystem of Qiyang,Chinese Academy of Agricultural Sciences,Qiyang,Hunan 426182,China;College of Resources and Environmental Sciences,Nanjing Agricultural University,Nanjing,Jiangsu 210095,China;College of Resources and Environmental Sciences,Nanjing Agricultural University,Nanjing,Jiangsu 210095,China |
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| Abstract: | Soil water dynamic change is significant to water cycle research and agricultural production. The least square support vector machine and the meteorological factors were used to train,test,and simulate soil water dynamic change in red soil region. Results showed that the least square support vector machine had more reliabilities and advantages of simulatio n perfor mance and mathematical meaning than the neural netw orks. Therefore, soil water dynamic change was simulated by the least square support vector machine and its trend was extracted by bior 3.3 with five layers of wavelet decomposition. The trend of soil water dynamic change can be divided into four stages which can provide a scientific basis for the water utilization and soil moisture prediction in the study region. |
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| Keywords: | least square support vector machine soil water dynamic simulation meteorological factor wavelet |
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