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A novel air pollution prediction system based on data processing, fuzzy theory, and multi-strategy improved

Zhirui Tian¹, Mei Gai^2,3

¹School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China.

Environmental Science and Pollution Research International

|April 4, 2023

Summary

This study introduces a novel hybrid system for predicting fine particulate matter (PM2.5) levels, improving both accuracy and reliability. The system effectively quantifies air pollution certainty and uncertainty for better public health protection.

Keywords:

Fuzzy theory Hybrid PM2.5 prediction system Improved multi-objective optimizer Neural network Variational mode decomposition

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Area of Science:

Environmental Science
Data Science
Atmospheric Chemistry

Background:

Fine particulate matter (PM2.5) is a critical air quality indicator with significant public health implications.
Traditional PM2.5 prediction methods struggle with data uncertainty, leading to unsatisfactory point and interval prediction accuracy.
Achieving desired interval coverage (PINC) in PM2.5 forecasting remains a challenge.

Purpose of the Study:

To develop an advanced hybrid PM2.5 prediction system capable of quantifying both certainty and uncertainty.
To enhance the accuracy of point predictions for PM2.5 concentrations.
To improve the reliability and coverage of interval predictions for PM2.5.

Main Methods:

A multi-strategy improved multi-objective crystal algorithm (IMOCRY) with chaotic mapping and screening operators for point prediction.
A combined neural network utilizing an unconstrained weighting method to further boost point prediction accuracy.
A novel interval prediction strategy combining fuzzy information granulation (FIG) and variational mode decomposition (VMD) to process data and extract high-frequency components.

Main Results:

The proposed IMOCRY algorithm demonstrates suitability for practical PM2.5 prediction applications.
The hybrid system achieves improved point prediction accuracy through the integration of neural networks.
The FIG-VMD approach yields fuzzy interval predictions with high coverage and narrow interval widths, addressing PINC limitations.
Experimental results validate the system's advanced nature, accuracy, generalization, and fuzzy prediction capabilities.

Conclusions:

The developed hybrid PM2.5 prediction system effectively addresses the limitations of traditional methods.
The system provides a robust solution for quantifying PM2.5 concentration certainty and uncertainty.
The findings confirm the system's practical applicability and potential for safeguarding respiratory health against air pollution.