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International Conference on Future Data and Security Engineering

FDSE 2021: Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications pp 264–275Cite as

Air Pollution Forecasting Using Regression Models and LSTM Deep Learning Models for Vietnam

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1500))

Abstract

In view of prediction techniques of hourly particulate matter (PM2.5) concentration in Viet Nam, this study’s aim is to apply Bi-directional Long Short-Term Memory (BLSTM) model to predict Air Quality Index (AQI) from PM2.5 concentration. The model is performed on data of hourly concentration of PM2.5 collected from 2 major Viet Nam cities: Hanoi and Ho Chi Minh City. The performance of BLSTM is evaluated by comparing with machine learning models CART, Random Forest, XGBoost using three metrics: RMSE, MAE and R2. This paper also aims to offer some time series’ parameter optimizations for future studies. Positive results are observed from the experiments that the proposed model outperforms other models by a large margin in all metrics.

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Acknowledgement

The authors are very thankful for the support provided by University of Information Technology, Vietnam National University Ho Chi Minh city (VNU-HCM) for the technical and financial support for this study.

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Authors and Affiliations

  1. University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Thuan Nguyen Dinh & Nam Phan Hoang

Authors
  1. Thuan Nguyen Dinh
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  2. Nam Phan Hoang
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Corresponding author

Correspondence to Thuan Nguyen Dinh .

Editor information

Editors and Affiliations

  1. HCMC University of Technology (HCMUT), Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Tokyo, Japan

    Makoto Takizawa

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Cite this paper

Nguyen Dinh, T., Phan Hoang, N. (2021). Air Pollution Forecasting Using Regression Models and LSTM Deep Learning Models for Vietnam. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_18

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  • DOI: https://doi.org/10.1007/978-981-16-8062-5_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8061-8

  • Online ISBN: 978-981-16-8062-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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