Application of stacked Transformer-XGBoost model and SHAP interpretability in air quality index prediction in Linfen City.

Jiafang Wei, Tianci Zhao, Rongsheng Wei, Xinjian Fan, Penghui Ji, Xiaohui Zhang

Abstract read

In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jiafang WeiSchool of Civil Engineering and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China.

Tianci ZhaoSchool of Civil Engineering and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China.

Rongsheng WeiSchool of Civil Engineering and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China.

Xinjian FanSchool of Civil Engineering and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China.

Penghui JiSchool of Civil Engineering and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China.

Xiaohui ZhangSchool of Computer and Artificial Intelligence, Lanzhou University of Technology, Lanzhou, Gansu 730050, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study proposes a stacked Transformer-XGBoost framework with Shapley Additive Explanations (SHAP) interpretability to improve Air Quality Index prediction in Linfen City, a heavily industrialized region characterized by complex pollution dynamics and strong seasonal variability. The model combines Transformer-based temporal feature extraction with XGBoost nonlinear regression through a temporally consistent out-of-fold stacking strategy, enabling effective learning of sequential dependencies and pollutant-meteorology interactions. Using historical air quality and meteorological data from 2013 to 2024, the proposed framework achieved superior predictive performance (R

Indexed as

Air Quality PredictionEnvironmental ManagementSHAP InterpretationTemporal Feature LearningTransformer-XGBoost Hybrid Model

Identifiers

PMID42621315

DOI10.1016/j.isci.2026.116396

PMCPMC13487283

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.