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基于BP神经网络的农田大气氨浓度预测

瞿英 王冕 董文旭 王玉恒

瞿英, 王冕, 董文旭, 王玉恒. 基于BP神经网络的农田大气氨浓度预测[J]. 中国生态农业学报(中英文), 2019, 27(4): 519-528. doi: 10.13930/j.cnki.cjea.181057
引用本文: 瞿英, 王冕, 董文旭, 王玉恒. 基于BP神经网络的农田大气氨浓度预测[J]. 中国生态农业学报(中英文), 2019, 27(4): 519-528. doi: 10.13930/j.cnki.cjea.181057
QU Ying, WANG Mian, DONG Wenxu, WANG Yuheng. Prediction of atmospheric ammonia concentration in farmlands using BP neural network[J]. Chinese Journal of Eco-Agriculture, 2019, 27(4): 519-528. doi: 10.13930/j.cnki.cjea.181057
Citation: QU Ying, WANG Mian, DONG Wenxu, WANG Yuheng. Prediction of atmospheric ammonia concentration in farmlands using BP neural network[J]. Chinese Journal of Eco-Agriculture, 2019, 27(4): 519-528. doi: 10.13930/j.cnki.cjea.181057

基于BP神经网络的农田大气氨浓度预测

doi: 10.13930/j.cnki.cjea.181057
基金项目: 

国家自然科学基金面上项目 C030601

中国科学院重点部署项目 ZDRW-ZS-2016-5-1

河北省高等学校科学技术研究项目 ZD2017029

河北省科技厅软科学研究计划项目 18457631D

详细信息
    作者简介:

    瞿英, 主要研究方向为决策理论与技术。E-mail:quying1973@126.com

    通讯作者:

    王玉恒, 主要研究方向为数据分析和风险管理。E-mail:xinxiwyh@126.com

  • 中图分类号: S19

Prediction of atmospheric ammonia concentration in farmlands using BP neural network

Funds: 

the National Natural Science Foundation of China C030601

the Key Deployment Project of Chinese Academy of Sciences ZDRW-ZS-2016-5-1

the Colleges and Universities Science and Technology Research Project of Hebei Province ZD2017029

the Soft Science Project of Hebei Science and Technology Department 18457631D

More Information
  • 摘要: 农业源氨排放是大气氨最主要的来源,其中氮肥施用是最主要的农业氨排放源之一。预测大气氨浓度的变化,确定影响大气氨排放的因素,可为科学管理农田,减轻环境污染提供参考。本文利用BP神经网络分析农田大气氨浓度及与各气象因素的关系,以便清晰地了解农田大气氨浓度的变化规律,为研究农田大气氨提供一种新的思路与方法。首先选取2015年5—10月的农田大气氨浓度数据及气象监测数据,建立以气象因素(气压、气温、相对湿度、降水量、风速和日照时数)为输入变量,农田大气氨浓度作为输出变量的预测模型。其次采用主成分分析法筛选出对农田大气氨浓度影响较大的气象因素,分别为气温、相对湿度、降水量和风速,然后把筛选出的4个主要因素和原来的6个因素分别作为BP神经网络预测模型的输入变量,利用神经网络模型对农田大气氨浓度进行预测。结果显示,农田大气氨浓度的实际值为0.148 5 mg·m-3,4个因素的预测值为0.159 4 mg·m-3,6个因素的预测值为0.173 2 mg·m-3,预测误差分别为7.35%、16.65%,并且4个因素的预测相对误差为1.4%~27.0%,而6个因素的预测相对误差为1.1%~45.0%。预测的农田大气氨浓度在前5 d内变化较大,但随着时间的推移,农田大气氨浓度逐渐变小趋于平缓,且预测值与实际值的变化趋势基本相符。利用4个因素作为输入变量建立预测模型,预测得到的农田大气氨浓度值比6个因素作为输入变量得到的农田大气氨浓度值与实际值更吻合,相对误差值较小。可见,通过主成分分析法去除冗余因素后建立的神经网络模型更加有效,预测结果比筛选之前的预测效果更好,所建立的模型对甄选关键因素具有较好的适用性,并且神经网络预测模型对农田大气氨浓度的预测精度较高。本文构建的农田大气氨浓度预测模型可为农田大气氨浓度分析及相关研究提供方法和思路上的指导。
  • 图  1  施肥(10月3日)前后冬小麦农田大气氨浓度的变化

    Figure  1.  Changes of ammonia concentration in winter wheat farmland before and after fertilization in the 3rd October

    图  2  农田大气氨浓度预测BP神经网络拓扑结构图

    Figure  2.  BP neural network topology for prediction of ammonia concentration in farmland

    图  3  不同情况下农田大气氨浓度BP神经网络预测值与实际值的变化

    Actual value:实际值; 4 factors: 4个因素预测值; 6 factors: 6个因素预测值。

    Figure  3.  Variation of actual and BP neural network predicted ammonia concentration in farmland

    "4 factors" and "6 factors" mean that the BP neural networks have 4 and 6 meteorological factors as the input neurons.

    图  4  不同情况下农田大气氨浓度预测值与实际值的相对误差变化

    4 factors: 4个因素相对误差; 6 factors: 6个因素相对误差。

    Figure  4.  Relative errors of predicted ammonia nitrogen concentration in farmland under different conditions

    "4 factors" and "6 factors" mean that the BP neural networks have 4 and 6 meteorological factors as the input neurons.

    表  1  试验区农田大气氨浓度与气象因素样本数据

    Table  1.   Sample data of atmospheric ammonia concentration and meteorological factors in farmland of the study area

    测定日期(年-月-日)
    Measuring date
    (year-month-day)
    测定时刻
    Measuring
    time
    大气氨浓度
    Atmospheric ammonia
    concentration (mg·m-3)
    气压
    Air pressure
    (hPa)
    气温
    Air temperature
    (℃)
    相对湿度
    Relative humidity
    (%)
    降水量
    Precipitation
    (mm)
    风速
    Wind speed
    (m·s-1)
    日照时数
    Sunshine hours
    (h)
    2015-05-01 8:00 0.546 3 999 18.7 84 0 4.0 0.5
    14 00 0.584 0 999 23.6 70 0 3.0 0
    20:00 0.341 7 1 000 16.0 91 0 3.5 0
    2015-05-02 8:00 0.302 3 1 002 17.2 78 9.9 0 2.0
    14:00 0.370 1 1 002 22.4 62 0 2.5 2.0
    20:00 0.410 6 1 001 18.0 83 0 0 0
    2015-05-03 8:00 0.345 7 1 002 16.7 92 0 2.7 0
    14:00 0.426 9 1 005 22.9 62 0 3.5 1.1
    20:00 0.426 5 1 005 18.1 72 0 2.0 0
    下载: 导出CSV

    表  2  气象因素对农田大气氨浓度影响的主成分特征值和累计贡献率

    Table  2.   Principal component eigenvalues and accumulated contribution rates of meteorological factors to atmospheric ammonia concentration in farmland

    主成分
    Principal
    component
    特征值
    Characteristic
    value
    贡献率
    Contribution
    rate (%)
    累计贡献率
    Accumulated
    contribution rate
    (%)
    1 2.229 37.156 37.156
    2 1.540 25.674 62.830
    3 0.927 15.455 78.285
    4 0.733 12.211 90.495
    下载: 导出CSV

    表  3  气象因素对农田大气氨浓度影响的主成分得分系数矩阵

    Table  3.   Principal component score coefficient matrix of meteorological factors affecting ammonia concentration in farmland

    因子
    Factor
    主成分F1
    Principal
    component
    F1
    主成分F2
    Principal
    component
    F2
    主成分F3
    Principal
    component
    F3
    主成分F4
    Principal
    component
    F4
    X1 -0.334 -0.378 0.246 0.077
    X2 0.882 0.260 -0.166 -0.196
    X3 -0.226 0.558 -0.090 0.155
    X4 0.019 0.326 0.907 0.174
    X5 0.264 -0.291 0.401 -0.607
    X6 0.265 -0.195 0.008 0.004
    X1:气压; X2:气温; X3:相对湿度; X4:降水量; X5:风速; X6:日照时数。X1: air pressure; X2: air temperature; X3: relative humidity; X4: precipitation; X5: wind speed; X6: sunshine hours.
    下载: 导出CSV
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  • 收稿日期:  2018-11-14
  • 录用日期:  2018-12-06
  • 刊出日期:  2019-04-01

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