王冕1,
董文旭2,
王玉恒1,,
1.河北科技大学经济管理学院 石家庄 050018
2.中国科学院遗传与发育生物学研究所农业资源研究中心 石家庄 050022
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计量
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被引次数:0
出版历程
收稿日期:2018-11-14
录用日期:2018-12-06
刊出日期:2019-04-01
Prediction of atmospheric ammonia concentration in farmlands using BP neural network
QU Ying1,,WANG Mian1,
DONG Wenxu2,
WANG Yuheng1,,
1. School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018, China
2. Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
Funds: the National Natural Science Foundation of ChinaC030601
the Key Deployment Project of Chinese Academy of SciencesZDRW-ZS-2016-5-1
the Colleges and Universities Science and Technology Research Project of Hebei ProvinceZD2017029
the Soft Science Project of Hebei Science and Technology Department18457631D
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Corresponding author:WANG Yuheng, E-mail: xinxiwyh@126.com
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摘要
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。
Abstract:The emission of ammonia from agricultural system is the main source of atmospheric ammonia and nitrogen fertilizer application is the main sources of ammonia emission in agriculture. The prediction of the changes in atmospheric ammonia concentration and the determination of the factors driving atmospheric ammonia emission will be benefit to the basis for scientific and rational farmland management and for control of environmental pollution. In this paper, BP neural network was used to analyze the concentration of ammonia in farmlands and its relationship with various meteorological factors. The aim was to better understand the changes in ammonia concentration in farmlands and provide new idea and method of study of ammonia in farmlands. First, farmland ammonia measured with Laser Analyzer and meteorological monitoring data from May to October 2015 were used to establish a model for the prediction of farmland ammonia with meteorological factors (air pressure, air temperature, relative humidity, precipitation, wind speed and sunshine hours) as input variables. Secondly, principal component analysis was used to screen meteorological factors with the strongest effect on the ammonia concentration in farmlands, including air temperature, relative humidity, precipitation and wind speed. Then four main factors and six original factors were used as input variables to predict ammonia concentration in farmlands in the region. The results showed that the actual ammonia concentration in farmlands was 0.148 5 mg·m-3, the predicted value based on four factors was 0.159 4 mg·m-3 (with predicted error of 7.35%) and the predicted value based on six factors was 0.173 2 mg·m-3 (with predicted error of 16.65%). The range of the relative error of four prediction factors was 1.4%-27.0% and that of six factors was 1.1%-45.0%. The predicted concentration of ammonia in farmlands varied greatly in the first five days, decreased gradually and apparently flattened out with time, which was basically consistent with the measured value. Eventually, four factors were used as input variables in building the prediction model. The predicted values of farmland ammonia concentration used four factors were more consistent with measured values than that used six factors. It was noted that the established neural network model after removing redundant factors by principal component analysis method was more effective, and the prediction results were better than those before screening. The model established had better applicability for selecting key factors, and the prediction accuracy was higher. The model constructed in this paper for predicting ammonia concentration in farmlands provided more accurate method and newer idea than before on the analysis of farmland ammonia concentration and the related research.
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图1施肥(10月3日)前后冬小麦农田大气氨浓度的变化
Figure1.Changes of ammonia concentration in winter wheat farmland before and after fertilization in the 3rd October
下载: 全尺寸图片幻灯片
图2农田大气氨浓度预测BP神经网络拓扑结构图
Figure2.BP neural network topology for prediction of ammonia concentration in farmland
下载: 全尺寸图片幻灯片
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。
Figure3.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.
下载: 全尺寸图片幻灯片
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。
Figure4.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.
下载: 全尺寸图片幻灯片表1试验区农田大气氨浓度与气象因素样本数据
Table1.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
Table2.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
Table3.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参考文献
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