Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm

doi:10.1016/j.ecoenv.2025.118764

Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm

2025-07-30

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AI

S

Seyed Vahid Razavi-Termeh

A

Abolghasem Sadeghi‐Niaraki

DOI：10.1016/j.ecoenv.2025.118764

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Abstract

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• This study optimized spatio-temporal O₃ modeling using RF and CS metaheuristic. • Fourteen environmental factors were analyzed to model seasonal O₃ distribution. • The spatio-temporal O₃ model achieved an AUC of 97 % in the spring season. • Seasonal O₃ predictions showed high accuracy using RF-CS in four seasons. • Altitude and wind direction were the most influential factors across seasons.

Keywords:

Ozone (O3) pollution

Spatio-temporal modelling

Ensemble machine learning

Big data

Public health

Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm

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