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Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation

机译：长短期记忆神经网络，用于空气污染物浓度预测：方法开发和评估

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摘要

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 mu m) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). (C) 2017 Elsevier Ltd. All rights reserved.
机译：空气污染物浓度预测是通过提供针对有害空气污染物的预警来保护公众健康的有效方法。但是，现有的空气污染物浓度预测方法无法有效地对长期依赖性进行建模，并且大多数情况下都忽略了空间相关性。本文提出了一种新颖的长短期记忆神经网络扩展（LSTME）模型，该模型固有地考虑了时空相关性，用于预测空气污染物浓度。长短期记忆（LSTM）层用于从历史空气污染物数据中自动提取固有的有用特征，并将辅助数据（包括气象数据和时间戳数据）合并到所提出的模型中以提高性能。 2014年1月1日至2016年5月28日在北京市12个空气质量监测站收集的每小时PM2.5（空气动力学直径小于或等于2.5微米的颗粒物）浓度数据用于验证有效性建议的LSTME模型。使用时空深度学习（STDL）模型，时延神经网络（TDNN）模型，自回归移动平均值（ARMA）模型，支持向量回归（SVR）模型和传统LSTM NN模型进行了实验，结果比较表明，LSTME模型优于其他基于统计的模型。另外，辅助数据的使用改善了模型性能。对于一小时的预测任务，所提出的模型表现良好，平均绝对百分比误差（MAPE）为11.93％。此外，即使在13-24小时的预测任务中（MAPE = 31.47％），我们也可以在不同的时间范围内进行多尺度预测，并获得令人满意的性能。 （C）2017 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Environmental Pollution》 |2017年第1期|997-1004|共8页
  • 作者

    Li Xiang; Peng Ling; Yao Xiaojing; Cui Shaolong; Hu Yuan; You Chengzeng; Chi Tianhe;

  • 作者单位

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Air pollutant concentration predictions; Long short-term memory neural network (LSTM NN); Recurrent neural network; Spatiotemporal correlation; Multiscale prediction;

    机译：空气污染物浓度预测;长短期记忆神经网络（LSTM NN）;递归神经网络;时空相关性;多尺度预测;

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