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Short-term air quality forecasting model based on hybrid RF-IACA-BPNN algorithm.

De-Wen Qiao1, Jian Yao2, Ji-Wen Zhang1

  • 1College of Architecture and Environment, Sichuan University, Chengdu, 610065, China.

Environmental Science and Pollution Research International
|January 31, 2022
PubMed
Summary

Accurate air quality forecasting for fine particulate matter (PM2.5) and ozone (O3) is crucial. A novel Random Forest-Improved Ant Colony Algorithm-Back Propagation Neural Network (RF-IACA-BPNN) model demonstrates superior performance in predicting hourly pollutant concentrations.

Keywords:
BP neural networkHourly PM2.5 and O3 concentration predictionImproved ant colony algorithmRandom forest

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Data Science

Background:

  • Despite air quality improvements, Chengdu faces persistent high concentrations of PM2.5 and O3.
  • These pollutants pose significant risks to human health and infrastructure, necessitating accurate forecasting.

Purpose of the Study:

  • To develop and validate an advanced air quality forecasting model for hourly PM2.5 and O3 concentrations.
  • To assess the model's performance against existing methods using real-world monitoring data.

Main Methods:

  • Utilized Random Forest (RF) for efficient input variable selection.
  • Implemented an Improved Ant Colony Algorithm (IACA) to optimize a Back Propagation Neural Network (BPNN) for enhanced convergence.
  • Tested the RF-IACA-BPNN model using datasets from two distinct monitoring station types and meteorological data.

Main Results:

  • The RF-IACA-BPNN model achieved the lowest statistical errors (MAE, RMSE, MAPE) and highest R² values compared to five other models.
  • Forecasting accuracy for PM2.5 was better at suburban stations, while O3 prediction was superior at downtown stations.
  • The model's effectiveness was validated across different station types and pollutant characteristics.

Conclusions:

  • The proposed RF-IACA-BPNN model is highly suitable for accurate hourly air quality prediction.
  • Understanding spatial variations in pollutant behavior (e.g., PM2.5 vs. O3) is essential for localized forecasting accuracy.
  • This advanced modeling approach offers a valuable tool for environmental management and public health protection.