• <menu id="4qso2"><strong id="4qso2"></strong></menu>
  • <nav id="4qso2"></nav>
  • 首页  >  科研动态  >  正文
    科研动态
    博士生王卫林的论文在REMOTE SENSING 刊出
    发布时间:2021-05-27 17:42:14     发布者:易真     浏览次数:

    标题: A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

    作者: Wang, WL (Wang, Weilin); Mao, WJ (Mao, Wenjing); Tong, XL (Tong, Xueli); Xu, G (Xu, Gang)

    来源出版物: REMOTE SENSING : 13 : 7 文献号: 1284 DOI: 10.3390/rs13071284 出版年: APR 2021

    摘要: Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 mu m) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R-2) = 0.74; root mean square error (RMSE) = 18.96 mu g/m(3)) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

    入藏号: WOS:000638790400001

    语言: English

    文献类型: Article

    作者关键词: air pollutant; PM2; 5; ConvLSTM; spatiotemporal correlation; long-term prediction

    KeyWords Plus: PM2.5 CONCENTRATIONS; LEARNING-MODEL; REGRESSION; PM10; QUALITY; OPTIMIZATION; EXPOSURE; STATION; AREA

    地址: [Wang, Weilin; Mao, Wenjing; Tong, Xueli] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

    [Xu, Gang] Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

    通讯作者地址: Xu, G (通讯作者),Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

    电子邮件地址: wangweilin@whu.edu.cn; wenjingmao@whu.edu.cn; xuelitong@whu.edu.cn; xugang@whu.edu.cn

    影响因子:4.509


    信息服务
    学院网站教师登录 学院办公电话 学校信息门户登录

    版权所有 ? 武汉大学资源与环境科学学院
    地址:湖北省武汉市珞喻路129号 邮编:430079 
    电话:027-68778381,68778284,68778296 传真:027-68778893    

    三级午夜理伦三级,琪琪网最新伦费观看2020动漫,办公室漂亮人妇在线观看 免费A级毛片茄子视频| 学生强伦姧老师在线观看国产| 亚洲jizzjizz妇女| 日本黄 色 成 人网站免费| 欧美激情第一欧美精品| 国产精品性夜天天拍拍| 少妇高潮太爽了在线观看免费| 久久午夜福利无码电影| 最新中文AV岛国无码免费播放| 国产亚洲欧美日韩亚洲中文色|