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于国强,李占斌,张 霞,李 鹏,刘海波.土壤水盐动态的BP神经网络模型及灰色关联分析[J].农业工程学报,2009,25(11):74-79.DOI:
土壤水盐动态的BP神经网络模型及灰色关联分析
投稿时间:2008-03-01  修订日期:2008-11-23
中文关键词:  土壤,BP,神经网络,敏感性因子,灰色关联分析
基金项目:国家科技支撑项目(2006BAD09B02)“黄土高原水土流失综合治理工程关键支撑技术研究”;中日合作项目(SBS-379)“沙漠化防治规划研究”; 国家重点基础研究发展计划项目(2007CB407206);西安理工大学优秀博士学位论文基金(106-210911)联合资助
作者单位
于国强 1. 西安理工大学西北水资源与环境生态教育部重点实验室西安 710048 
李占斌 1.西安理工大学西北水资源与环境生态教育部重点实验室西安 710048 2.中国科学院水利部水土保持研究所黄土高原土壤侵蚀与旱地农业国家重点实验室杨凌 712100 
张 霞 3. 陕西省环境科学研究设计院西安 710061 
李 鹏 1. 西安理工大学西北水资源与环境生态教育部重点实验室西安 710048 
刘海波 1. 西安理工大学西北水资源与环境生态教育部重点实验室西安 710048 
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中文摘要:以陕西洛惠渠灌区实测数据为例,引用3层前馈型BP网络建模方法,对灌区综合条件下土壤水盐动态进行研究,采用附加动量法和学习速率自适应调整策略对反向传播算法进行改造;在此基础上运用缺省因子检验法分析了土壤含盐量和土壤碱度对输入层各因子的敏感性,并采用灰色关联法加以验证。结果表明,人工神经网络模型具有较高的精度,能够很好地定量描述土壤水盐动态变化与其影响因子之间的响应关系;土壤含水率、地下水含盐量和蒸发量是影响土壤水盐动态的主要敏感因子,各因子之间相互作用,形成了复杂条件下的耦合关系。灰色关联法进一步验证了各因子的敏感程度。将以上方法相结合,可为分析浅地下水埋深条件下作物生育期内土壤水盐动态规律提供有效可行的方法,是对传统土壤水盐动态研究方法的补充与完善。
Yu Guoqiang,Li Zhanbin,Zhang Xia,Li Peng,Liu Haibo.Dynamic simulation of soil water-salt using BP neural network model and grey correlation analysis[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2009,25(11):74-79.DOI:
Dynamic simulation of soil water-salt using BP neural network model and grey correlation analysis
Author NameAffiliation
Yu Guoqiang 1. Key Laboratory of Northwest Water Resources and Environment Ecology Xi’an University of Technology, Xi’an 710048, China 
Li Zhanbin 1. Key Laboratory of Northwest Water Resources and Environment Ecology Xi’an University of Technology, Xi’an 710048, China
2. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China 
Zhang Xia 3. Research and Design Institute of Environmental Science of Shaanxi Province, Xi’an 710061, China 
Li Peng 1. Key Laboratory of Northwest Water Resources and Environment Ecology Xi’an University of Technology, Xi’an 710048, China 
Liu Haibo 1. Key Laboratory of Northwest Water Resources and Environment Ecology Xi’an University of Technology, Xi’an 710048, China 
Key words:soils, back propagation, neural networks, sensitiveness factors, grey correlation analysis
Abstract:Soil water-salt dynamic under natural-artificial-biological conditions was studied with measured data of Luohui trench irrigation district in Shaanxi Province based on application of backpropagation(BP) networks of three layers, and then the additional momentum method and self adaptive tactic for training were adopted to feed forward BP neural networks. On the basis of the condition above, a sensitivity analysis about soil salt content and soil alkalinity was conducted according to each input factor by using default factor method, and the grey correlation analysis method was applied to certify the results. The results showed that the artificial neural networks model could express quantitatively the response relationship between groundwater dynamic and various factors with sufficient high accuracy. Soil water content, salt concentration of groundwater, and evaporation capacity were the main sensitive factors for soil water-salt dynamic in this irrigation district, the interaction amongst various factors formed coupling relationship under the complicated condition. The grey correlation analysis method could further verify the sensitivity degree amongst various factors. The combination of the above methods provides feasible method for analyzing the rules of soil water-salt dynamic under the condition of shallow groundwater depth during crop growing season, and it is complement and perfection for the traditional research methods of groundwater water-salt dynamic.
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