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

Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, Armin Sorooshian2

  • 1Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.

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Summary

This study introduces a novel spatio-temporal model using ensemble machine learning and Cuckoo search to predict ozone (O3) pollution. The accurate O3 risk maps provide crucial data for public health and environmental policy.

Keywords:
Big dataEnsemble machine learningOzone (O(3)) pollutionPublic healthSpatio-temporal modelling

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Area of Science:

  • Environmental Science
  • Public Health
  • Data Science

Background:

  • Ozone (O3) pollution poses significant global environmental and public health risks, impacting respiratory and cardiovascular health.
  • Accurate spatio-temporal modeling is essential for assessing and predicting O3 pollution levels to mitigate its adverse effects.

Purpose of the Study:

  • To develop a novel spatio-temporal model for O3 pollution assessment and prediction.
  • To integrate an ensemble machine learning algorithm with a swarm-based metaheuristic optimization algorithm for enhanced O3 modeling.

Main Methods:

  • Utilized surface-based O3 data and 14 environmental factors from Tehran, Iran (2018-2022).
  • Employed Random Forest (RF) as the base ensemble machine learning model.
  • Optimized the RF model using the Cuckoo Search (CS) metaheuristic algorithm.

Main Results:

  • Achieved high accuracy in O3 risk map predictions across seasons: 95.2% (autumn), 97% (spring), 96.7% (summer), and 95.7% (winter).
  • The Receiver Operating Characteristic (ROC) curve evaluation confirmed strong model performance.

Conclusions:

  • The novel integrated model effectively predicts O3 pollution with high accuracy.
  • Findings offer actionable insights for policymakers and public health officials to address O3 impacts on health and the environment.