Haze forecasting via deep LSTM

Fan Feng, Jikai Wu, Wei Sun, Yushuang Wu, Hua Kang Li, Xingguo Chen*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

5 Citations (Scopus)


PM 2.5 is a crucial indicator of haze pollution, which can cause problems in respiratory systems. Accurate PM 2.5 concentration forecasting systems are essential for human beings to take precautions. State-of-the-art methods including support vector regression (SVR), artificial neural network (ANN) and Bayesian, try to forecast PM 2.5 concentrations of the following 3 days via building an approximation from weather features to PM 2.5 concentration. However, the performances of these methods are poor because they ignore the essence of the problem: PM 2.5 concentration is the product of a time series. This paper aims to propose more accurate forecasting algorithms to forecast PM 2.5 concentration. First, we employ the recurrent neural network with Long Short Term Memory kernel to handle the time series forecasting. Secondly, in order to further improve the performance, a convolutional neural network (CNN) is utilized as feature extractor to generate input for LSTM. Two models are proposed to handle the forecast for the following 3 and 7 days: (i) based on 2 days’ weather features and PM 2.5 concentrations; (ii) based on 4 days’ (including 2 days of this year, the day of last year, and the day two years ago) weather features and PM 2.5 concentrations. Finally, all experiments are compared with the root of mean squared errors (RMSE) for each city and averaged root of mean squared errors (ARMSE) of all cities. Experiments are tested on two datasets: one with hourly meteorological data and daily air-pollution data of 104 cities in east China from 2013 to 2017, the other with both hourly meteorological and air-pollution data in 5 cities from 2010 to 2015. Experimental results show that the proposed methods significantly outperform the state-of-the-art.

Original languageEnglish
Title of host publicationWeb and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings
EditorsJianliang Xu, Yoshiharu Ishikawa, Yi Cai
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319968896
Publication statusPublished - 2018
Externally publishedYes
Event2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 - Macau, China
Duration: 23 Jul 201825 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10987 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018


  • Convolutional neural network
  • Haze forecasting
  • LSTM


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