Air Quality Prediction in Shaanxi Province: A GBDT Model Study from the Digital-Intelligent Governance Perspective

Zhen Ma; Zihao Wang

doi:10.6919/ICJE.202512_11(12).0005

Authors

Zhen Ma
Zihao Wang

DOI:

https://doi.org/10.6919/ICJE.202512_11(12).0005

Keywords:

GBDT; Air Quality Forecasting; Environmental Digital Intelligence Governance.

Abstract

This study constructs an air quality machine learning prediction model based on air quality monitoring and meteorological index data from Shaanxi Province, drawing on the concept of environmental digital intelligence governance. After data cleaning, feature extraction, and standardization processing, the performance of the decision tree, random forest, and GBDT algorithm is compared. The results show that the GBDT model has the best prediction effect, with an R² of 0.9832, which can effectively capture air quality changes, and PM10, PM2.5, and SO₂ are the main air quality influencing factors. The research results can provide data support for regional environmental protection and precise pollution control, and also provide reference cases for the practical application of digital intelligence technology in environmental governance.

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References

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