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

Javier Linkolk López-Gonzales¹, Rodrigo Salas^2,3, Daira Velandia^4,5

¹Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru.

Entropy (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

This study enhances air quality prediction by combining Singular Spectrum Analysis (SSA) with Long Short-Term Memory (LSTM) neural networks. The hybrid approach improves forecasting accuracy by separately analyzing and predicting signal and noise components.

Keywords:

air quality artificial neural networks hybrid method singular spectrum analysis

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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.