Volume 9 Issue 1
Jan.  2019
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Article Navigation >  Journal of Environmental Engineering Technology  > 2019  >  9(1): 14-18
ZHAO Wenyi, XIA Lisha, GAO Guangkuo, CHENG Li. PM2.5 prediction model based on weighted KNN-BP neural network[J]. Journal of Environmental Engineering Technology, 2019, 9(1): 14-18. doi: 10.3969/j.issn.1674-991X.2019.01.003
Citation: ZHAO Wenyi, XIA Lisha, GAO Guangkuo, CHENG Li. PM2.5 prediction model based on weighted KNN-BP neural network[J]. Journal of Environmental Engineering Technology, 2019, 9(1): 14-18. doi: 10.3969/j.issn.1674-991X.2019.01.003
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PM2.5 prediction model based on weighted KNN-BP neural network

doi: 10.3969/j.issn.1674-991X.2019.01.003

  • School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China

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  • Corresponding author: Lisha XIA E-mail: lisaxss@163.com
  • Received Date: 2018-08-12
  • Publish Date: 2019-01-20
    Abstract
  • Through the weighted KNN-BP neural network method determined by membership function , the dynamic real-time prediction model of PM2.5 concentration was established. The concentration of six pollutants, i.e. PM2.5, PM10, NO2, CO, O3 and SO2, six meteorological data including weather condition, temperature, pressure, humidity, wind speed and wind direction in the first hour, as well as the days of a week and the hours of the days for projection were regarded as the dimensions of the KNN instance. Three nearest neighbors were selected and, according to the Euclidean distance obtained, the membership weight of each neighbor point variable determined. Finally, the dimension of all nearest neighbor points were taken as the input layer of BP neural network, and the next hour PM2.5 concentration to be predicted as the output layer data. The method avoided the problem that the traditional BP neural network method failed to reflect the influence of the data in the historical window on the current predicting. The data of 2014-05-01 from 00:00 to 23:00 2014-09-10 in Dongcheng District monitoring station in Beijing was tested. The results showed that the prediction model with weighted KNN-BP neural network had the lowest deviation compared with other methods, and the stability showed the best. Therefore, this model is an effective method for the PM2.5 real time prediction.

     

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    • References(19)
  • [1]
    王金南 . 控制PM2.5污染:中国路线图与政策机制[M]. 北京: 科学出版社, 2016.
    [2]
    李岚淼, 李龙国, 李乃稳 . 城市雾霾成因及危害研究进展[J]. 环境工程, 2017,35(12):92-97.

    LI L M, LI L G, LI N W . Studies review on cause and damage of urban haze[J]. Environmental Engineering, 2017,35(12):92-97.
    [3]
    杨继东, 章逸然 . 空气污染的定价:基于幸福感数据的分析[J]. 世界经济, 2014,37(12):162-188.
    [4]
    LEE K H, KIM Y J, KIM M J . Characteristics of aerosol observed during two severe haze events over Korea in June and October 2004[J]. Atmospheric Environment, 2006,40(27):5146-5155.
    doi: 10.1016/j.atmosenv.2006.03.050
    [5]
    彭斯俊, 沈加超, 朱雪 . 基于ARIMA模型的PM2.5预测[J]. 安全与环境工程, 2014,21(6):125-128.
    doi: 10.3969/j.issn.1671-1556.2014.06.023

    PENG S J, SHEN J C, ZHU X . Forecast of PM2.5 based on the ARIMA model[J]. Safety and Environmental Engineering, 2014,21(6):125-128. doi: 10.3969/j.issn.1671-1556.2014.06.023
    [6]
    黄思, 唐晓, 徐文帅 , 等. 利用多模式集合和多元线性回归改进北京PM10预报[J]. 环境科学学报, 2015,35(1):56-64.
    doi: 10.13671/j.hjkxxb.2014.0728

    HUANG S, TANG X, XU W S , et al. Application of ensemble forecast and linear regression method in improving PM10 forecast over Beijing areas[J]. Acta Scientiae Circumstantiae, 2015,35(1):56-64. doi: 10.13671/j.hjkxxb.2014.0728
    [7]
    艾洪福, 潘贺, 李迎斌 . 关于空气雾霾PM2.5含量预测优化研究[J]. 计算机仿真, 2017,34(1):392-395.
    doi: 10.3969/j.issn.1006-9348.2017.01.085

    AI H F, PAN H, LI Y B . Research on optimization of PM2.5 content prediction in air haze[J]. Computer Simulation, 2017,34(1):392-395. doi: 10.3969/j.issn.1006-9348.2017.01.085
    [8]
    孙宝磊, 孙暠, 张朝能 , 等. 基于BP神经网络的大气污染物浓度预测[J]. 环境科学学报, 2017,37(5):1864-1871.
    doi: 10.13671/j.hjkxxb.2016.0391

    SUN B L, SUN H, ZHANG C N , et al. Forecast of air pollutant concentrations by BP neural network[J]. Acta Scientiae Circumstantiae, 2017,37(5):1864-1871. doi: 10.13671/j.hjkxxb.2016.0391
    [9]
    DÍAZ-ROBLES L A, ORTEGA J C, FU J S , et al. A hybrid ARIMA and artificial neural networksmodel to forecast particulate matter in urban areas:the case of Temuco,Chile[J]. Atmospheric Environment, 2008,42:8331-8340.
    doi: 10.1016/j.atmosenv.2008.07.020
    [10]
    艾洪福, 石莹 . 基于BP人工神经网络的雾霾天气预测研究[J]. 计算机仿真, 2015,32(1):402-405.
    doi: 10.3969/j.issn.1006-9348.2015.01.085

    AI H F, SHI Y . Study on prediction of haze based on BP neural network[J]. Computer Simulation, 2015,32(1):402-405. doi: 10.3969/j.issn.1006-9348.2015.01.085
    [11]
    赵晨曦, 王云琦, 王玉杰 , 等. 北京地区冬春PM2.5和PM10污染水平时空分布及其与气象条件的关系[J]. 环境科学, 2014,35(2):418-427.

    ZHAO C X, WANG Y Q, WANG Y J , et al. Temporal and spatial distribution of PM2.5 and PM10 pollution status and the correlation of particulate matters and meteorological factors during winter and spring in Beijing[J]. Environmental Science, 2014,35(2):418-427.
    [12]
    刘金培, 汪官镇, 陈华友 , 等. 基于VAR模型的PM2.5与其影响因素动态关系研究:以西安市为例[J]. 干旱区资源与环境, 2016,30(5):78-84.
    doi: 10.13448/j.cnki.jalre.2016.151

    LIU J P, WANG G Z, CHEN H Y , et al. Dynamic relationship between PM2.5 and its influence factors in Xi’an City based on the VAR model[J]. Journal of Arid Land Resources and Environment, 2016,30(5):78-84. doi: 10.13448/j.cnki.jalre.2016.151
    [13]
    迈尔·舍恩伯格, 库克耶 . 大数据时代[M]. 杭州: 浙江人民出版社, 2013.
    [14]
    潘竟虎, 张文, 王春娟 . 2011—2013年中国雾霾易发生期间API的分布格局[J]. 环境工程学报, 2016,10(3):1340-1348.

    PAN J H, ZHANG W, WANG C J . Spatial pattern of urban air pollution during large-scale range haze based on fractal net evolution approach and GIS in China[J]. Chinese Journal of Environmental Engineering, 2016,10(3):1340-1348.
    [15]
    杨健, 宋冰, 谭帅 , 等. 时序约束NPE算法在化工过程故障检测中的应用[J]. 化工学报, 2016,67(12):5131-5139.
    doi: 10.11949/j.issn.0438-1157.20160543

    YANG J, SONG B, TAN S , et al. Time constrained NPE for fault detection in chemical processes[J]. CIESC Journal, 2016,67(12):5131-5139. doi: 10.11949/j.issn.0438-1157.20160543
    [16]
    王钰, 郭其一, 李维刚 . 基于改进BP神经网络的预测模型及其应用[J].计算机测量与控制, 2005(1):39-42.
    doi: 10.3321/j.issn:1671-4598.2005.01.013

    WANG Y, GUO Q Y, LI W G . Predictive model based on improved BP neural networks and it’s application[J].Computer Measurement & Control,2005(1):39-42. doi: 10.3321/j.issn:1671-4598.2005.01.013
    [17]
    ZHENG Y, YI X W, LI M , et al. Forecasting fine-grained air quality based on big data[M]. Sydney:Association for Computing Machinery, 2015: 1231-1239.
    [18]
    ZHENG Y, CAPRA L, WOLFSON O , et al. Urban computing:concepts,methodologies and applications[J]. ACM Transaction on Intelligent Systems and Technology, 2014,5(3):1-55.
    doi: 10.1145/2629592
    [19]
    ZHENG Y, LIU F R, HSIEH H P .U-air:when urban air quality inference meets big data[M].Chicago:Association for Computing Machinery, 2013: 1436-1444.
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