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Related Experiment Video

Updated: Nov 18, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

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Intelligent multivariable air-quality forecasting system based on feature selection and modified evolving interval

Jianzhou Wang¹, Hongmin Li¹, Hufang Yang¹

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

Environmental Pollution (Barking, Essex : 1987)

|February 5, 2021

Summary

This study introduces an advanced air quality index (AQI) forecasting system using a novel neural network and feature selection. The intelligent hybrid model accurately predicts AQI by addressing uncertainties and climate factors.

Keywords:

Air quality forecasting Feature selection Fuzzy neural network Interval type-2 fuzzy sets Multi-objective optimization algorithm

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Published on: October 11, 2018

Area of Science:

Environmental Science
Artificial Intelligence
Data Science

Background:

Air quality index (AQI) forecasting faces challenges due to high nonlinearity and noise.
Existing methods struggle to effectively address uncertainties and fuzziness in AQI prediction.
Accurate AQI forecasting is crucial for environmental management and public health.

Purpose of the Study:

To develop an intelligent hybrid air quality forecasting system.
To improve AQI prediction accuracy by incorporating climate influencing factors.
To effectively manage uncertainties and fuzziness in the forecasting process.

Main Methods:

A second-stage feature selection model was proposed to identify optimal input variables and reduce redundancy.
A modified evolving interval type-2 quantum fuzzy neural network (eIT2QFNN) was developed.
A novel multi-objective chaotic Bonobo optimizer algorithm was introduced to enhance the eIT2QFNN.

Main Results:

The proposed feature selection model effectively extracts influencing variables and removes redundant information.
The modified eIT2QFNN, enhanced by the novel optimizer, demonstrated improved AQI prediction capabilities.
Diebold-Mariano and modified Diebold-Mariano tests confirmed significant improvements in modeling performance.

Conclusions:

The developed intelligent hybrid system offers high accuracy and a compact structure for AQI forecasting.
The system effectively handles uncertainties and fuzziness inherent in air quality data.
This approach provides an effective tool for robust air quality management.