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Related Experiment Videos

Evaluating deep learning time series models for PM_2.5 forecasting across diverse horizons.

Ling Zeng¹, Runan Dong^1,2, Meng Yuan^1,2

¹Geomathematics Key Laboratory of Sichuan Province, Chengdu Technological University, Chengdu 610059, China.

|February 18, 2026

Summary

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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Transformer-LSTM models excel at forecasting PM2.5 (particulate matter) in Chengdu. Integrating meteorological data and ensuring complete seasonal training significantly enhances prediction accuracy for air quality management.

Area of Science:

Environmental Science
Data Science
Atmospheric Chemistry

Background:

Urban air pollution, especially PM2.5, presents a significant public health risk in cities like Chengdu, China.
Geographical factors (basin topography) and high emission levels intensify PM2.5 concentrations, necessitating accurate forecasting.
Existing forecasting methods may not fully capture the complex dynamics of PM2.5 influenced by various environmental factors.

Purpose of the Study:

To evaluate the performance of four deep learning time series algorithms (LSTM, CNN-LSTM, Transformer, Transformer-LSTM) for PM2.5 forecasting.
To compare univariate and multivariate model configurations using auxiliary pollutants and meteorological data.
To assess the impact of data completeness and prediction horizons on forecasting accuracy.

Main Methods:

Keywords:

Atmosphere modelling Atmospheric chemistry Atmospheric science Environmental health Environmental science Machine learning Pollution

Related Experiment Videos

Utilized two years of daily PM2.5 data from Chengdu, China (November 2022-October 2024).
Compared LSTM, CNN-LSTM, Transformer, and Transformer-LSTM models in univariate and multivariate settings.
Incorporated auxiliary pollutants (CO, NO2, O3, SO2) and meteorological factors (temperature, pressure, precipitation, wind speed).
Evaluated models across different forecasting horizons (monthly, seasonal, half-year, annual) and data completeness scenarios.

Main Results:

Transformer-LSTM demonstrated superior performance, indicated by higher R-squared and lower MAE% and RMSE% values.
Augmenting models with meteorological factors yielded better results than using only auxiliary pollutants.
Complete seasonal training datasets significantly improved model performance.
Gaps exceeding three months in training data reduced prediction reliability due to evolving PM2.5 dynamics.

Conclusions:

Deep learning models, particularly Transformer-LSTM, are effective for PM2.5 forecasting in complex urban environments.
Meteorological data integration is crucial for enhancing PM2.5 prediction accuracy.
Maintaining seasonal data completeness and timely predictions are vital for reliable air quality management strategies.