What does this research mean for the field?

The proposed OMVMD-WLSSVM-GRM integrative machine learning model achieves highly accurate and reliable air quality index forecasting (R ≈ 0.97-0.99) across major Chinese cities, outperforming traditional benchmark models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a hybrid machine learning model for accurate air quality index forecasting in Chinese cities.

April 4, 2026Sustainable Futures0 citationsOpen Access

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China major cities air quality index forecasting using integrative machine learning models: A perception for sustainable cities

ZYZaher Mundher Yaseen

Key Points

To develop a hybrid machine learning model for accurate air quality index forecasting in Chinese cities.
Developed a hybrid model combining weighted least squares support vector machine and generalized regression model.
Applied multivariate variational mode decomposition for data processing and forecasting accuracy enhancement.
Employed a light gradient boosting machine with a differential evolution algorithm for feature identification.
Achieved high correlation coefficients (R ≈ 0.99, 0.97, and 0.98) for air quality forecasting in Beijing, Wuhan, and Xi’an.
Demonstrated low maximum absolute errors (Max AE ≈ 97.3, 78.3, and 78.3).
Provided narrower uncertainty ranges, confirming model reliability.

Abstract

• Innovative hybrid machine learning model was developed for air quality prediction. • Multivariate variational mode decomposition model was adopted for data processing. • Improved differential evolution algorithm was used to identify the essential predictors. • Three major cities in China i.e., Beijing, Wuhan, and Xi’an were investigated. • The final product of the complementary machine learning approved its potential. Accurate air quality forecasting is essential process for establishing sustainable cities and healthy communities. The present study introduces an innovative integrative machine learning (ML) model to overcome the limitations of traditional ML models and prior studies in capturing the complicated nonlinearity of air quality index (AQI). The integration of weighted least squares support vector machine with generalized regression model (WLSSVM-GRM) was adopted as predictive model for AQI. An optimized multivariate variational mode decomposition (OMVMD) method was introduced to decompose the input variables and increase the forecasting accuracy. Further, the proposed model uses an efficient light gradient boosting machine (LGBM) model enhanced by the self-adaptive teaching learning-based with differential evolution (SATLDE) algorithm to identify the most significant features influencing AQI. The proposed OMVMD-WLSSVM-GRM model showed an outstanding performance in forecasting AQI across Beijing, Wuhan, and Xi’an in China with correlation coefficient ( R ≈ 0.99, 0.97, and 0.98), outperforming other benchmark models. Also, the proposed model attained low extreme errors ( M a x A E ≈ 97.3, 78.3 and 78.3), and better vicis symmetric distance stability (VSD ≈ 342.6, 632.3, and 638.8). Uncertainty analysis confirmed the proposed model reliability, with smaller forecasting intervals and narrower uncertainty ranges. Overall, the developed model provided reliable air quality forecasting and possibly enhanced environmental management and preserved the health of the community.

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China major cities air quality index forecasting using integrative machine learning models: A perception for sustainable cities

Key Points

Abstract

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