Details

Autor(en) / Beteiligte

• Nikpour, Parsa
• Shafiei, Mahdis
• Khatibi, Vahid

Titel

Gelato: a new hybrid deep learning-based Informer model for multivariate air pollution prediction

Ist Teil von

• Environmental science and pollution research international, 2024-04, Vol.31 (20), p.29870-29885

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2024

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accurate prediction of air pollutants such as CO 2 , O 3 , and PM2.5. Most of the models used in the literature are deep learning models with Transformers being the best for time series prediction. However, there is still a need to enhance accuracy in air pollution prediction using Transformers. Alongside the need for increased accuracy, there is a significant demand for predicting a broader spectrum of air pollutants. To encounter this challenge, this paper proposes a new hybrid deep learning-based Informer model called “Gelato” for multivariate air pollution prediction. Gelato takes a leap forward by taking several air pollutants into consideration simultaneously. Besides introducing new changes to the Informer structure as the base model, Gelato utilizes Particle Swarm Optimization for hyperparameter optimization. Moreover, XGBoost is used at the final stage to achieve minimal errors. Applying the proposed model on a dataset containing eight important air pollutants, including CO 2 , O 3 , NO, NO 2 , SO 2 , PM10, NH 3 , and PM2.5, the Gelato performance is assessed. Comparing the results of Gelato with other models shows Gelato’s superiority over them, proving it is a high-confidence model for multivariate air pollution prediction.