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An ensemble learning based hybrid model and framework for air pollution forecasting.

Yue-Shan Chang1, Satheesh Abimannan2, Hsin-Ta Chiao3

  • 1Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan. ysc@mail.ntpu.edu.tw.

Environmental Science and Pollution Research International
|July 5, 2020
PubMed
Summary

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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This summary is machine-generated.

This study introduces a novel hybrid model for enhanced air pollution forecasting. The proposed ensemble learning approach significantly improves prediction accuracy compared to traditional machine learning and deep learning methods.

Area of Science:

  • Environmental Science
  • Data Science
  • Computer Science

Background:

  • Air pollution poses significant risks, necessitating accurate forecasting models.
  • Existing machine learning and deep learning models (GBTR, SVR, LSTM) face challenges in prediction performance enhancement.
  • Ensemble learning has shown promise in improving predictive accuracy in various domains.

Purpose of the Study:

  • To develop and validate a hybrid model for superior air pollution forecasting accuracy.
  • To integrate diverse machine learning and deep learning models using an ensemble approach.
  • To establish a robust framework for short-term (1-8 hours) air quality prediction.

Main Methods:

  • A stacking-based ensemble learning scheme was employed, utilizing Pearson correlation coefficient to integrate models.
Keywords:
Air pollution forecastingEnsemble learningGBTRLSTMPM2.5Pearson correlation coefficientSVR

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  • A framework combining Spark+Hadoop machine learning and TensorFlow deep learning was constructed.
  • The hybrid model's performance was experimentally evaluated against Gradient Boosted Tree Regression (GBTR), Support Vector Machine-based Regression (SVR), and Long Short-Term Memory (LSTM) models.

    • Main Results:

    • The proposed hybrid model demonstrated superior predictive performance compared to individual GBTR, SVR, and LSTM models.
    • Experimental results confirmed the enhanced accuracy of the ensemble approach for air pollution forecasting.
    • The framework successfully integrated various models for forecasting pollutant concentrations.

    Conclusions:

    • The developed hybrid model offers a significant advancement in air pollution forecasting accuracy.
    • Ensemble learning, when combined with a robust framework, effectively addresses limitations of traditional models.
    • This approach provides a reliable tool for short-term air quality prediction, aiding environmental management efforts.