Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks

Area of Science:

• Environmental Science
• Data Science
• Artificial Intelligence

Background:

• Time series analysis is crucial for environmental monitoring.
• Neural networks offer advanced capabilities for complex data patterns.
• Accurate air quality prediction is vital for public health and policy.

Purpose of the Study:

• To improve the precision of air quality prediction.
• To introduce a novel hybrid methodology integrating SSA and LSTM.
• To evaluate the performance of the proposed hybrid model.

Main Methods:

• Singular Spectrum Analysis (SSA) was employed to decompose time series data into trend, seasonal, and noise components.
• Recurrent Neural Network Long Short-Term Memory (LSTM) was utilized for predictive modeling.
• A hybrid approach combined SSA for signal separation and LSTM for forecasting, with separate predictions for signal and noise components.

Main Results:

• The hybrid SSA-LSTM model demonstrated superior performance in air quality prediction compared to other methods.
• Decomposition of time series allowed for more accurate forecasting of individual components.
• The integration effectively handled both deterministic (trend, seasonal) and stochastic (noise) elements.

Conclusions:

• The hybrid SSA-LSTM method offers a significant advancement in air quality forecasting accuracy.
• This approach provides a robust framework for time series analysis in environmental applications.
• The findings suggest potential for broader application in complex environmental data prediction.