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[Predictive Model for O3 in Shanghai Based on the KZ Filtering Technique and LSTM]
Ling-Xia Wu1, Jun-Lin An1, Dan Jin2
1Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Huan Jing Ke Xue= Huanjing Kexue
|October 25, 2024
Summary
This study improved ozone (O3) prediction accuracy by decomposing O3 sequences and selecting key meteorological factors using enhanced maximal minimal redundancy (mRMR) and support vector regression (SVR). The LSTM model accurately predicted high ozone periods.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Data Science
Context:
- Ozone (O3) pollution poses significant environmental and health risks, necessitating accurate long-term prediction models.
- Traditional O3 prediction methods often struggle with complex temporal dynamics and meteorological influences.
- Identifying key meteorological drivers is crucial for enhancing O3 forecasting accuracy.
Purpose:
- To develop an improved method for predicting long-term ozone (O3) concentration series.
- To enhance feature selection for meteorological variables influencing O3 levels using enhanced maximal minimal redundancy (mRMR) and support vector regression (SVR).
- To assess the predictive performance of a long short-term memory (LSTM) network for high O3 concentration periods.
Summary:
- A Kolmogorov-Zurbenko (KZ) filter was employed to decompose O3 sequences, separating baseline and short-term components.
- The enhanced mRMR technique combined with SVR identified pressure, temperature, humidity, boundary layer height, and wind direction as optimal meteorological features.
- An LSTM model was applied to predict high O3 concentration periods in Shanghai, demonstrating strong performance with R2 values ranging from 0.83 to 0.86.
Impact:
- Decomposing the O3 sequence significantly improved prediction accuracy compared to standard methods.
- Feature selection effectively identified crucial meteorological variables, maintaining model predictive performance.
- The study provides a robust framework for enhancing O3 forecasting, aiding in pollution control and public health strategies.
