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Related Experiment Videos

Long-term time-series pollution forecast using statistical and deep learning methods.

Pritthijit Nath1, Pratik Saha2, Asif Iqbal Middya1

  • 1Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.

Neural Computing & Applications
|April 12, 2021
PubMed
Summary
This summary is machine-generated.

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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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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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Statistical methods like SARIMA and Holt-Winters outperform deep learning for forecasting long-term air pollution trends (PM2.5, PM10) in Kolkata. This aids environmental policy development.

Area of Science:

  • Environmental Science
  • Data Science
  • Time Series Analysis

Background:

  • Air pollution is a critical global issue requiring accurate long-term forecasting for effective environmental policy.
  • Existing methods often focus on short-term predictions, leaving a gap in long-term trend analysis.
  • Forecasting particulate matter (PM2.5 and PM10) trends is crucial for urban planning and public health.

Purpose of the Study:

  • To comparatively evaluate statistical and deep learning methods for long-term air pollution forecasting.
  • To forecast future PM2.5 and PM10 trends in Kolkata for the next two years.
  • To identify the most effective forecasting approach for informing environmental policy.

Main Methods:

  • Utilized historical air pollution data (PM2.5, PM10) from Kolkata monitoring stations.
Keywords:
Air pollutionDeep learningLong-term forecastStatistical modelsTime-series analysis

Related Experiment Videos

  • Applied time-series analysis techniques to identify pollution patterns.
  • Compared performance of statistical models (AR, SARIMA, Holt-Winters) against deep learning models (Stacked, Bi-directional, Auto-encoder, Convolutional LSTM).

    • Main Results:

    • Statistical methods, specifically Auto-Regressive (AR), Seasonal Auto-Regressive Integrated Moving Average (SARIMA), and Holt-Winters, demonstrated superior performance.
    • Deep learning models, including Stacked, Bi-directional, Auto-encoder, and Convolutional Long Short-Term Memory (LSTM) networks, were outperformed.
    • The findings are based on analysis of limited historical data.

    Conclusions:

    • For long-term air pollution forecasting with limited data, traditional statistical methods are currently more effective than advanced deep learning approaches.
    • The study provides valuable insights for policymakers in Kolkata regarding environmental strategies.
    • Further research with larger datasets could explore the potential of deep learning methods.