Source Jouranl of CSCD
Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Environmental Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
DONG Hao, SUN Lin, OUYANG Feng. PREDICTION OF PM2.5 CONCENTRATION BASED ON INFORMER[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 48-54,62. doi: 10.13205/j.hjgc.202206006
Citation: DONG Hao, SUN Lin, OUYANG Feng. PREDICTION OF PM2.5 CONCENTRATION BASED ON INFORMER[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 48-54,62. doi: 10.13205/j.hjgc.202206006

PREDICTION OF PM2.5 CONCENTRATION BASED ON INFORMER

doi: 10.13205/j.hjgc.202206006
  • Received Date: 2021-11-09
    Available Online: 2022-09-01
  • Publish Date: 2022-09-01
  • For improving the low accuracy of the existing models for time series prediction of PM2.5 concentration,a Seq2Seq multi-step PM2.5 concentration prediction model for single-site based on Informer was proposed.With a series of air pollutant data and meteorological data in the past,Informer could make a forecast for PM2.5 concentration in the future.The constructed model extracted the information of the input sequence based on the probsparse self-attention mechanism,which could widely capture the long-range dependency of the input sequence and model the complex nonlinearity between features,to improve the prediction accuracy eventually.The hourly air pollutant data and meteorological data of Beijing from 2015 to 2019 were used for training,validation and testing.Compared with RNN,LSTM and other existing models,the MAE,RMSE and R2 metrics of Informer were the best for the time series prediction of PM2.5 concentration in the next 1 to 6 hours,and then a more accurate prediction was realized.
  • [1]
    郭新彪,魏红英.大气PM2.5对健康影响的研究进展[J].科学通报,2013,58(13):1171-1177.
    [2]
    宋宇,唐孝炎,方晨,等.北京市能见度下降与颗粒物污染的关系[J].环境科学学报,2003,23(4):468-471.
    [3]
    师华定,高庆先,张时煌,等.空气污染对气候变化影响与反馈的研究评述[J].环境科学研究,2012,25(9):974-980.
    [4]
    蒋锋,乔雅倩.基于样本熵和优化极限学习机的PM2.5浓度预测[J].统计与决策,2021,37(3):166-171.
    [5]
    秦思达,王帆,王堃,等.基于WRF-CMAQ模型的辽宁中部城市群PM2.5化学组分特征[J].环境科学研究,2021,34(6):1277-1286.
    [6]
    杜勃莹,马云峰,王琦,等.基于WRF-Chem模型的沈阳市颗粒物扩散特征和成因分析[J].环境工程,2021,39(2):89-97

    ,104.
    [7]
    付倩娆.基于多元线性回归的雾霾预测方法研究[J].计算机科学,2016,43(增刊1):526-528.
    [8]
    刘宗伟,周彩丽,马冬梅,等.自回归移动平均模型在预测PM2.5中的应用[J].预防医学论坛,2016,22(8):582-584.
    [9]
    朱晏民,徐爱兰,孙强.基于深度学习的空气质量预报方法新进展[J].中国环境监测,2020,36(3):10-18.
    [10]
    李建新,刘小生,刘静,等.基于MRMR-HK-SVM模型的PM2.5浓度预测[J].中国环境科学,2019,39(6):2304-2310.
    [11]
    段大高,赵振东,梁少虎,等.基于LSTM的PM2.5浓度预测模型[J].计算机测量与控制,2019,27(3):215-219.
    [12]
    赵文芳,林润生,唐伟,等.基于深度学习的PM2.5短期预测模型[J].南京师大学报(自然科学版),2019,42(3):32-41.
    [13]
    SCHUSTER M, PALIWAL K K. Bidirectional recurrent neural networks[J]. IEEE transactions on Signal Processing, 1997, 45(11):2673-2681.
    [14]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
    [15]
    ZHOU H, ZHANG S, PENG J, et al. Informer:beyond efficient transformer for long sequence time-series forecasting[C]//Proceedings of AAAI, 2021.
    [16]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems, 2017:5998-6008.
    [17]
    陆瑶,杨洁,邵智娟,等.基于阶段式时序注意力网络的PM2.5鲁棒预测[J].环境工程,2021,39(10):93-100.
    [18]
    张婷婷.北京典型地区大气PM2.5的理化特征及来源初步解析[D].北京:北京建筑大学,2018.
    [19]
    于建华,虞统,魏强,等.北京地区PM10和PM2.5质量浓度的变化特征[J].环境科学研究,2004,17(1):45-47.
    [20]
    XIE Y Y,ZHAO B,ZHANG L, et al.Spatiotemporal variations of PM2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO2,NO2,CO and O3[J].Particuology,2015,20(3):141-149.
    [21]
    杨兴川,赵文吉,熊秋林,等.2016年京津冀地区PM2.5时空分布特征及其与气象因素的关系[J].生态环境学报,2017,26(10):1747-1754.
    [22]
    黄春桃,范东平,卢集富,等.基于深度学习的广州市大气PM2.5和PM10浓度预测[J].环境工程,2021,39(12):135-140.
    [23]
    王菲. LSTM循环神经网络的研究进展与应用[D].哈尔滨:黑龙江大学,2021.
    [24]
    张宸鹏.回复式神经网络若干关键问题研究[D].成都:电子科技大学,2021.
    [25]
    赵滨,刘斌.基于Stacking的地面PM2.5浓度估算[J].环境工程,2020,38(2):153-159.
    [26]
    于伸庭,刘萍.基于长短期记忆网络-卷积神经网络(LSTM-CNN)的北京市PM2.5浓度预测[J].环境工程,2020,38(6):176-180

    ,66.
    [27]
    李晓理,梅建想,张山.基于改进粒子群优化BP_Adaboost神经网络的PM2.5浓度预测[J].大连理工大学学报,2018,58(3):316-323.
  • Relative Articles

    [1]CHU Yangyang, LI Hui, ZHU Yanping, HAN Xiaomeng, SHU Shihu. A REVIEW OF RESEARCH PROGRESS OF PREDICTION MODELS FOR DISINFECTION BY-PRODUCTS: EMPIRICAL MODELS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(7): 38-48. doi: 10.13205/j.hjgc.202407004
    [2]WU Yulun, LI Zemin, CHENG Xiaoqian, QIU Guanglei, WEI Chaohai. PREDICTION OF NITROGEN REMOVAL PERFORMANCE AND IDENTIFICATION OF KEY PARAMETERS OF PARTIAL NITRIFICATION/PARTIAL DENITRIFICATION-ANAMMOX PROCESS BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(9): 180-190. doi: 10.13205/j.hjgc.202409017
    [3]XIE Qi, XIA Fei, YUAN Bo. PREDICTION OF PM2.5 CONCENTRATION IN XI’AN BASED ON CEEMDAN-SE-BiLSTM MODEL[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(8): 105-115. doi: 10.13205/j.hjgc.202408013
    [4]ZHANG Tao, WANG Xiahui, BI Erping, HUANG Guoxin, YANG Ruijie. GROUNDWATER VULNERABILITY EVALUATION AND RISK CONTROL IN A CERTAIN AREA IN NORTHERN GUANGDONG PROVINCE BASED ON BP NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 270-277. doi: 10.13205/j.hjgc.202312034
    [5]LI Yuanyuan, LIU Hailong. PREDICTION OF TOTAL PHOSPHORUS IN RIVERS BASED ON ATTENTION MECHANISM OF TEMPORAL CONVOLUTIONAL NETWORKS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(5): 163-171. doi: 10.13205/j.hjgc.202305022
    [6]YUAN Ye, GAO Jun, ZHANG Lulu, CHEN Tianming, DING Cheng. RESEARCH PROGRESS ON INFLUENCING FACTORS AND THEIR PREDICTION MODELS OF HYDROGEN SULFIDE GENERATION IN MUNICIPAL SEWAGE PIPELINES[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 69-77. doi: 10.13205/j.hjgc.202311013
    [7]PEI Lifeng, CHEN Weijie, XU Jingsheng, LÜ Lu. MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 84-92,140. doi: 10.13205/j.hjgc.202311015
    [8]XU Runze, CAO Jiashun, FANG Fang. RESEARCH PROGRESS ON N2O RECYCLING AND DATA-DRIVEN MODELING IN WASTEWATER TREATMENT PROCESSES[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 107-115. doi: 10.13205/j.hjgc.202206014
    [9]HUANG Yanpeng, WANG Yuanhao, WANG Chao, LIU Weijiang, WANG Hong, LV Guangfeng, LIN Sijie, HU Qing. CHARACTERISTICS ANALYSIS AND ZONING CONTROL OF GROUNDWATER POLLUTION BASED ON SELF-ORGANIZING MAPS AND K-MEANS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 31-41,47. doi: 10.13205/j.hjgc.202206004
    [10]RUI Dongni, MA Yanyan, YE Lin. APPLICATION OF MACHINE LEARNING METHODS IN WASTEWATER TREATMENT SYSTEMS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 145-153. doi: 10.13205/j.hjgc.202206019
    [11]LIU Yanbiao, QIAO Jianzhi, YOU Shijie. RESEARCH PROGRESS ON APPLICATIONS OF MACHINE LEARNING IN CARBON-BASED ENVIRONMENTAL FUNCTIONAL MATERIALS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 182-187. doi: 10.13205/j.hjgc.202206023
    [12]HU Xiangang, LI Jiawei, LI Jiaqiang, JIN Hongye, YU Fubo. SCIENTIFIC QUESTIONS ON THE BIOLOGICAL EFFECTS OF NANOMATERIALS BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 171-181. doi: 10.13205/j.hjgc.202206022
    [13]HU Song, LIU Guohong, HE Ying, YAN Jiachen, CHEN Hanle, YAN Xiliang, YAN Bing. PREDICTION ON PHOTOELECTRIC CONVERSION EFFICIENCY OF ORGANIC PHOTOVOLTAIC MATERIALS USING END-TO-END DEEP LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 188-193. doi: 10.13205/j.hjgc.202206024
    [14]HUANG Chun-tao, FAN Dong-ping, LU Ji-fu, LIAO Qi-feng. PREDICTION OF PM2.5 AND PM10 CONCENTRATION IN GUANGZHOU BASED ON DEEP LEARNING MODEL[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(12): 135-140. doi: 10.13205/j.hjgc.202112020
    [15]LU Yao, YANG Jie, SHAO Zhi-juan, ZHU Cong-cong. PM2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(10): 93-100. doi: 10.13205/j.hjgc.202110013
    [16]HE Zhe-xiang, LI Lei. AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 111-119. doi: 10.13205/j.hjgc.202103016
    [17]LI Zhi-sheng, LIANG Xi-guan, JIN Yu-kai, ZHANG Hua-gang, OU Yao-chun. A COMPARATIVE STUDY ON EDICTIVE EFFECT OF PM2.5 IN BEIJING BASED ON TREE MODELS[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(6): 106-113. doi: 10.13205/j.hjgc.202106016
    [18]YU Shen-ting, LIU Ping. LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029
    [19]LIANG Tao, XIE Gao-feng, MI Da-bin, JIANG Wen. PREDICTION OF PM10 CONCENTRATION BASED ON CEEMDAN-SE AND LSTM NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(2): 107-113. doi: 10.13205/j.hjgc.202002015
  • Cited by

    Periodical cited type(14)

    1. 江雨燕,黄体臣,甘如美江,王付宇. 融合二次分解的深度学习模型在PM_(2.5)浓度预测中的应用. 安全与环境学报. 2025(01): 296-309 .
    2. 杨帆,毛腾跃,占伟. 基于PCA-Informer~+模型的周期性甲烷菌体浓度预测研究. 中南民族大学学报(自然科学版). 2025(03): 393-399 .
    3. 赵涛,叶世榕,罗歆琪,夏朋飞. GNSS-IR潮位反演中高仰角数据质量控制方法. 武汉大学学报(信息科学版). 2024(01): 68-76 .
    4. 张雅波,陈春晖. 融合情绪分析和Informer-ARIMA模型的比特币价格预测方法. 现代信息科技. 2024(09): 131-135 .
    5. 陆钊,龙法宁,陈国年. 基于时间序列的发电机设备异常分析. 现代信息科技. 2024(12): 121-124 .
    6. 王鲁君,孙永华,刘洪涛,于秋红,郝浚杰. 基于多尺度特征Informer模型的受热面积灰预测研究. 山东电力高等专科学校学报. 2024(03): 35-40 .
    7. 马愈昭,张宇航,王凌飞. TimeGAN-Informer长时机场能见度预测. 安全与环境学报. 2024(07): 2517-2527 .
    8. 何志铧,熊祖强. 基于Informer神经网络的工作面矿压预测研究. 矿业研究与开发. 2024(07): 142-148 .
    9. 刘妙男,王魏,胡显辉,许德昊. 基于因果卷积和Informer模型的城市公交客流预测. 控制工程. 2024(08): 1445-1454 .
    10. 安昱宁,朱四富,刘静,杜立伟,刘长青. 基于深度学习的污水处理厂出水总磷预测方法. 工业水处理. 2024(10): 143-150 .
    11. 何宇涵. 基于自注意力机制的PM_(2.5)长时间尺度预测. 长江信息通信. 2024(10): 72-75 .
    12. 王伟,王海云,黄晓芳. 基于Informer的风电机组叶根载荷预测. 水力发电. 2023(09): 85-89 .
    13. 董子敬,李凡,孙宏,朱梦媛,范博艺. 基于Informer模型的进近阶段工作负荷管理胜任力评估. 飞行力学. 2023(05): 81-87 .
    14. 蒲维,杨毅强,张渊博,付江涛,宋弘. 基于NGO-VMD-FCBF-Informer的电力负荷组合预测模型. 智能计算机与应用. 2023(11): 135-141 .

    Other cited types(17)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-040102030405060
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 16.8 %FULLTEXT: 16.8 %META: 80.4 %META: 80.4 %PDF: 2.9 %PDF: 2.9 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 15.5 %其他: 15.5 %其他: 0.4 %其他: 0.4 %China: 0.9 %China: 0.9 %Kao-sung: 0.4 %Kao-sung: 0.4 %Seattle: 0.3 %Seattle: 0.3 %[]: 0.1 %[]: 0.1 %三亚: 0.1 %三亚: 0.1 %上海: 1.6 %上海: 1.6 %东莞: 1.9 %东莞: 1.9 %东营: 0.1 %东营: 0.1 %临汾: 0.1 %临汾: 0.1 %九江: 0.3 %九江: 0.3 %六安: 0.8 %六安: 0.8 %北京: 3.8 %北京: 3.8 %十堰: 0.1 %十堰: 0.1 %南京: 1.6 %南京: 1.6 %南充: 0.1 %南充: 0.1 %南通: 0.1 %南通: 0.1 %厦门: 0.1 %厦门: 0.1 %台北: 0.5 %台北: 0.5 %台州: 0.1 %台州: 0.1 %合肥: 1.6 %合肥: 1.6 %哈尔滨: 0.1 %哈尔滨: 0.1 %嘉兴: 0.3 %嘉兴: 0.3 %天津: 0.6 %天津: 0.6 %太原: 0.3 %太原: 0.3 %娄底: 0.1 %娄底: 0.1 %宜春: 0.8 %宜春: 0.8 %宣城: 0.3 %宣城: 0.3 %宿州: 0.1 %宿州: 0.1 %常州: 0.1 %常州: 0.1 %常德: 0.1 %常德: 0.1 %广州: 1.4 %广州: 1.4 %廊坊: 0.1 %廊坊: 0.1 %张家口: 1.4 %张家口: 1.4 %德罕: 0.4 %德罕: 0.4 %成都: 0.6 %成都: 0.6 %扬州: 0.1 %扬州: 0.1 %昆明: 0.5 %昆明: 0.5 %晋城: 0.3 %晋城: 0.3 %曲靖: 0.1 %曲靖: 0.1 %朝阳: 1.0 %朝阳: 1.0 %杭州: 0.4 %杭州: 0.4 %格兰特县: 0.3 %格兰特县: 0.3 %武汉: 1.8 %武汉: 1.8 %永州: 0.5 %永州: 0.5 %沈阳: 0.1 %沈阳: 0.1 %沧州: 0.1 %沧州: 0.1 %济南: 0.9 %济南: 0.9 %济源: 0.3 %济源: 0.3 %湖州: 0.4 %湖州: 0.4 %湘潭: 0.6 %湘潭: 0.6 %漯河: 0.9 %漯河: 0.9 %烟台: 0.1 %烟台: 0.1 %石嘴山: 0.1 %石嘴山: 0.1 %石家庄: 1.0 %石家庄: 1.0 %福州: 0.3 %福州: 0.3 %芒廷维尤: 34.7 %芒廷维尤: 34.7 %芝加哥: 1.5 %芝加哥: 1.5 %苏州: 1.0 %苏州: 1.0 %荆州: 0.3 %荆州: 0.3 %衡水: 0.3 %衡水: 0.3 %衢州: 0.5 %衢州: 0.5 %襄阳: 0.1 %襄阳: 0.1 %西宁: 6.1 %西宁: 6.1 %西安: 1.1 %西安: 1.1 %贵阳: 0.5 %贵阳: 0.5 %运城: 1.3 %运城: 1.3 %遵义: 0.1 %遵义: 0.1 %郑州: 0.3 %郑州: 0.3 %鄂州: 3.3 %鄂州: 3.3 %重庆: 0.6 %重庆: 0.6 %长沙: 0.4 %长沙: 0.4 %青岛: 0.6 %青岛: 0.6 %马鞍山: 0.4 %马鞍山: 0.4 %黄冈: 0.3 %黄冈: 0.3 %其他其他ChinaKao-sungSeattle[]三亚上海东莞东营临汾九江六安北京十堰南京南充南通厦门台北台州合肥哈尔滨嘉兴天津太原娄底宜春宣城宿州常州常德广州廊坊张家口德罕成都扬州昆明晋城曲靖朝阳杭州格兰特县武汉永州沈阳沧州济南济源湖州湘潭漯河烟台石嘴山石家庄福州芒廷维尤芝加哥苏州荆州衡水衢州襄阳西宁西安贵阳运城遵义郑州鄂州重庆长沙青岛马鞍山黄冈

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (641) PDF downloads(30) Cited by(31)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return