Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction.

By Jiachen Zhao, Fang Deng, Yeyun Cai, Jie Chen. Chemosphere, 2019

369 citations recorded by the source corpus.

People have been suffering from air pollution for a decade in China, especially from PM2.5 (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-driven model, called as long short-term memory - fully connected (LSTM-FC) neural network, to predict PM2.5 contamination of a specific air quality monitoring station over 48 h using historical air quality data, meteorological data, weather forecast data, and the day of the week. Our predictive model consists of two components: (1) Using a long short-term memory (LSTM)-based temporal simulator to model the local variation of PM2.5 contamination and (2) Using a neural network-based spatial combinatory to capture spatial dependencies between the PM2.5 contamination of central station and that of neighbor stations. We evaluate our model on a dataset containing records of 36 air quality monitoring stations in Beijing from 2014/05/01 to 2015/04/30 and compare it with artificial neural network (ANN) and long short-term memory (LSTM) models on the same dataset. The results show that our LSTM-FC neural network model gives a better predictive performance.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/511c4a7153ad1f263377059c7720944ff0b0b76e

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In Environmental Engineering

  1. M. Hua
  2. Xu Wu
  3. Taoying Li

In Health, Toxicology and Mutagenesis

  1. M. Hua
  2. Xu Wu
  3. Taoying Li

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