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基于小波变换和动态神经网络的温室黄瓜蒸腾速率预测

DOI: 10.7685/j.issn.1000-2030.2014.05.023, PP. 143-152

Keywords: 温室,黄瓜,蒸腾,小波变换,动态神经网络,时间序列,预测

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Abstract:

针对作物蒸腾速率与温室环境参数间非线性耦合时延性关系,以温室环境参数空气温度、空气湿度、太阳辐射度、土壤温度、叶面温度、土壤含水量的时间序列为输入量,温室黄瓜蒸腾速率时间序列为输出量,采用小波分解重构方法,分别建立低频时间序列和高频时间序列的非线性自回归动态神经网络(NARX)子网络预测模型,以子网络的预测叠加值为蒸腾速率预测值。结果表明1层小波分解重构的低频时间序列A1和高频时间序列D1的子网络预测值与蒸腾速率分解重构目标值间相关性决定系数R2分别为0.949和0.853,平均绝对误差(MAE)分别为5.36和2.00g?h-1。2层小波分解重构的低频时间序列A2和高频时间序列D2的子网络预测值与蒸腾速率分解重构目标值间相关性决定系数R2分别为0.983和0.849,MAE分别为2.88和2.56g?h-1。1层小波分解重构的时间序列的NARX子网络预测值合成值(A1+D1),2层小波分解重构的时间序列的NARX子网络预测值合成值(A2+D2+D1)和未小波分解重构的原时间序列的NARX预测值与蒸腾速率测量值间相关性决定系数R2分别为0.945、0.974和0.857,MAE分别为5.76、4.42和10.09g?h-1。小波分解重构的高频和低频时间序列预测合成,能够提高时间序列的预测准确性。同时采用相同网络结构的BP神经网络和NAR动态神经网络预测蒸腾速率时间序列,其预测值与测量值间决定系数R2分别为0.596和0.839,MAE分别为19.55和9.45g?h-1。NARX预测性能优于NAR和BP神经网络的预测性能,能够应用该方法预测温室黄瓜的蒸腾速率。该方法可推广至多变量非线性强耦合时延性系统中的变量预测。

References

[1]  Miranda F R,Gondim R S,Costa C A G. Evapotranspiration and crop coefficients for Tabasco pepper(Capsicum frutescens L.)[J]. Agricultural Water Management,2006,82(2):237-246
[2]  Wang S,Boulard T. Greenhouse crop transpiration simulation from external climate conditions[J]. Agricultural and Forest Meteorology,2000,100(1):25-34
[3]  Demrati H,Boulard T,Fatnassi H,et al. Microclimate and transpiration of a greenhouse banana crop[J]. Biosystems Engineering,2007,98(1):66-78
[4]  汪小?,丁为民,罗卫红,等. 南方现代温室能耗预测模型的建立与分析[J]. 南京农业大学学报,2006,29(1):116-120. doi:10.7685/j.issn.1000-2030.2006.01.026
[5]  [Wang X C,Ding W M,Luo W H,et al. An energy prediction model for modern greenhouse in the south of China[J]. Journal of Nanjing Agricultural University,2006,29(1):116-120(in Chinese with English abstract)]
[6]  汪小?,丁为民,罗卫红,等. 温室小气候测量试验设计及其夏季蒸腾研究[J]. 农业机械学报,2003,34(4):86-89
[7]  [Wang X C,Ding W M,Luo W H,et al. Design experiment to measure microclimate and study transpiration in summer in greenhouse[J]. Transactions of the Chinese Society for Agricultural Machinery,2003,34(4):86-89(in Chinese with English abstract)]
[8]  汪小?,罗卫红,丁为民,等. 南方现代化温室黄瓜夏季蒸腾研究[J]. 中国农业科学,2002,35(11):1390-1395
[9]  [Wang X C,Luo W H,Ding W M,et al. Cucumber canopy transpiration in subtropical modern greenhouse under summer climate condition[J]. Scientia Agricultura Sinica,2002,35(11):1390-1395(in Chinese with English abstract)]
[10]  戴剑锋,金亮,罗卫红,等. 长江中下游Venlo型温室番茄蒸腾模拟研究[J]. 农业工程学报,2006,22(3):99-103
[11]  [Dai J F,Jin L,Luo W H,et al. Simulation of greenhouse tomato canopy transpiration in Yangtze River Delta[J]. Transactions of the CSAE,2006,22(3):99-103(in Chinese with English abstract)]
[12]  刘浩,段爱旺,孙景生,等. 基于Penman-Monteith方程的日光温室番茄蒸腾量估算模型[J]. 农业工程学报,2011,27(9):208-213
[13]  [Liu H,Duan A W,Sun J S,et al. Estimating model of transpiration for greenhouse tomato based on Penman-Monteith equation[J]. Transactions of the CSAE,2011,27(9):208-213(in Chinese with English abstract)]
[14]  彭致功. 日光温室滴管条件下小气候变化和植株蒸腾规律的研究[D]. 北京:中国农业科学院,2002
[15]  [Peng Z G. Study on the environmental factors and plant transpiration under drip irrigation solar greenhouse[D]. Beijing:Chinese Academy of Agricultural Sciences,2002(in Chinese with English abstract)]
[16]  姚勇哲,李建明,张荣,等. 温室番茄蒸腾量与其影响因子的相关分析及模型模拟[J]. 应用生态学报,2012,23(7):1869-1874
[17]  [Yao Y Z,Li J M,Zhang R,et al. Greenhouse tomato transpiration and its affecting factors:correlation analysis and model simulation[J]. Chinese Journal of Applied Ecology,2012,23(7):1869-1874(in Chinese with English abstract)]
[18]  Harmanto S V M,Babel M S,Tantau H J. Water requirement of drip irrigated tomatoes grown in greenhouse in tropical environment[J]. Agricultural Water Management,2005,71(3):225-242
[19]  Yuan B Z,Kang Y,Nishiyama S. Drip irrigation scheduling for tomatoes in unheated greenhouse[J]. Irrigation Science,2001,20(3):149-154
[20]  Yuan B Z,Sun J,Nishiyama S. Effect of drip irrigation on strawberry growth and yield inside a plastic greenhouse[J]. Biosystems Engineering,2004,87(2):237-245
[21]  刘祖贵,段爱旺,吴海卿,等. 水肥调配施用对温室滴灌番茄产量及水分利用效率的影响[J]. 中国农村水利水电,2003(1):10-12
[22]  [Liu Z G,Duan A W,Wu H Q,et al. Impacts of water and fertilizer allocation on tomato yield and water use efficiency in drip irrigation greenhouse[J]. China Rural Water and Hydropower,2003(1):10-12(in Chinese with English abstract)]
[23]  Hanson B R,May D M. Crop coefficients for drip-irrigated processing tomato[J]. Agricultural Water Management,2006,81(3):381-399
[24]  牛勇,刘洪禄,吴文勇,等. 基于大型称重式蒸渗仪的日光温室黄瓜蒸腾规律研究[J]. 农业工程学报,2011,27(1):52-56
[25]  [Niu Y,Liu H L,Wu W Y,et al. Cucumber transpiration by large-scale weighting lysimeter in solar greenhouse[J]. Transactions of the CSAE,2011,27(1):52-56(in Chinese with English abstract)]
[26]  王世谦,苏娟,杜松怀. 基于小波变换和神经网络的短期风电功率预测方法[J]. 农业工程学报,2010,26(增刊2):125-129
[27]  [Wang S Q,Su J,Du S H. A method of short-term wind power forecast based on wavelet transform and neural network[J]. Transactions of the CSAE,2010,26(Suppl 2):125-129(in Chinese with English abstract)]
[28]  佟长福,史海滨,包小庆,等. 基于小波分析理论组合模型的农业需水量预测[J]. 农业工程学报,2011,27(5):93-98
[29]  [Tong C F,Shi H B,Bao X Q,et al. Application of a combined model based on wavelet analysis for predicting crop water requirement[J]. Transactions of the CSAE,2011,27(5):93-98(in Chinese with English abstract)]
[30]  柴琳娜,屈永华,张立新,等. 基于自回归神经网络的时间序列叶面积指数估算[J]. 地球科学进展,2009,24(7):756-768
[31]  [Chai L N,Qu Y H,Zhang L X,et al. Estimating time series leaf area index based on recurrent neural networks[J]. Advances in Earth Science,2009,24(7):756-768(in Chinese with English abstract)]

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