Research and application of a novel selective stacking ensemble model based on error compensation and parameter optimization for AQI prediction.
Environ Res
; 247: 118176, 2024 Apr 15.
Article
in En
| MEDLINE
| ID: mdl-38215922
ABSTRACT
With the ongoing process of industrialization, the issue of declining air quality is increasingly becoming a critical concern. Accurate prediction of the Air Quality Index (AQI), considered as an all-inclusive measure representing the extent of pollutants present in the atmosphere, is of paramount importance. This study introduces a novel methodology that combines stacking ensemble and error correction to improve AQI prediction. Additionally, the reptile search algorithm (RSA) is employed for optimizing model parameters. In this study, four distinct regional AQI data containing a collection of 34864 data samples are collected. Initially, we perform cross-validation on ten commonly used single models to obtain prediction results. Then, based on evaluation indices, five models are selected for ensemble. The results of the study show that the model proposed in this paper achieves an improvement of around 10% in terms of accuracy when compared to the conventional model. Thus, the model introduced in this study offers a more scientifically grounded approach in tackling air pollution.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Air Pollutants
/
Air Pollution
/
Environmental Pollutants
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Environ Res
Year:
2024
Document type:
Article
Country of publication:
Países Bajos