Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm
The future of ozone (O3) pollution presents significant environmental and public health challenges worldwide. High O3 levels can harm respiratory health, exacerbating conditions such as asthma and increasing the risk of cardiovascular diseases. Addressing these challenges requires advanced spatio-te...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Ecotoxicology and Environmental Safety |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325011091 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849389739615977472 |
|---|---|
| author | Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Armin Sorooshian Lingbo Liu Shuming Bao Soo-Mi Choi |
| author_facet | Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Armin Sorooshian Lingbo Liu Shuming Bao Soo-Mi Choi |
| author_sort | Seyed Vahid Razavi-Termeh |
| collection | DOAJ |
| description | The future of ozone (O3) pollution presents significant environmental and public health challenges worldwide. High O3 levels can harm respiratory health, exacerbating conditions such as asthma and increasing the risk of cardiovascular diseases. Addressing these challenges requires advanced spatio-temporal modeling techniques to assess and predict O3 pollution levels accurately. This research fills a crucial gap in current modeling approaches by proposing a novel methodology that integrates an ensemble machine-learning algorithm with swarm-based metaheuristic optimization algorithm. The study uses surface-based ozone data and data for 14 environmental factors for Tehran, Iran between 2018 and 2022 to develop a spatio-temporal model of O3 pollution. The ensemble machine learning algorithm was selected as the base model, specifically the Random Forest (RF). To enhance its performance, a metaheuristic algorithm (Cuckoo search (CS) algorithm) was utilized for optimization. The evaluation of ozone risk maps, measured using the Receiver Operating Characteristic (ROC) curve, demonstrated strong performance across seasons. Specifically, the accuracy of the O3 risk map was 95.2 % for autumn, 97 % for spring, 96.7 % for summer, and 95.7 % for winter. This research provides actionable information for policymakers and public health officials to mitigate the impacts of O3 pollution on human health and the environment. |
| format | Article |
| id | doaj-art-3be023a0112e4a31ae8ebfa1dff91fbc |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-3be023a0112e4a31ae8ebfa1dff91fbc2025-08-20T03:41:52ZengElsevierEcotoxicology and Environmental Safety0147-65132025-09-0130211876410.1016/j.ecoenv.2025.118764Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithmSeyed Vahid Razavi-Termeh0Abolghasem Sadeghi-Niaraki1Armin Sorooshian2Lingbo Liu3Shuming Bao4Soo-Mi Choi5Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of KoreaDept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea; Corresponding author.Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USACenter for Geographic Analysis, Harvard University, Cambridge, USAChina Data Institute, University of Michigan, Ann Arbor, MI 48108, USADept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of KoreaThe future of ozone (O3) pollution presents significant environmental and public health challenges worldwide. High O3 levels can harm respiratory health, exacerbating conditions such as asthma and increasing the risk of cardiovascular diseases. Addressing these challenges requires advanced spatio-temporal modeling techniques to assess and predict O3 pollution levels accurately. This research fills a crucial gap in current modeling approaches by proposing a novel methodology that integrates an ensemble machine-learning algorithm with swarm-based metaheuristic optimization algorithm. The study uses surface-based ozone data and data for 14 environmental factors for Tehran, Iran between 2018 and 2022 to develop a spatio-temporal model of O3 pollution. The ensemble machine learning algorithm was selected as the base model, specifically the Random Forest (RF). To enhance its performance, a metaheuristic algorithm (Cuckoo search (CS) algorithm) was utilized for optimization. The evaluation of ozone risk maps, measured using the Receiver Operating Characteristic (ROC) curve, demonstrated strong performance across seasons. Specifically, the accuracy of the O3 risk map was 95.2 % for autumn, 97 % for spring, 96.7 % for summer, and 95.7 % for winter. This research provides actionable information for policymakers and public health officials to mitigate the impacts of O3 pollution on human health and the environment.http://www.sciencedirect.com/science/article/pii/S0147651325011091Ozone (O3) pollutionSpatio-temporal modellingEnsemble machine learningBig dataPublic health |
| spellingShingle | Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Armin Sorooshian Lingbo Liu Shuming Bao Soo-Mi Choi Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm Ecotoxicology and Environmental Safety Ozone (O3) pollution Spatio-temporal modelling Ensemble machine learning Big data Public health |
| title | Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm |
| title_full | Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm |
| title_fullStr | Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm |
| title_full_unstemmed | Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm |
| title_short | Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm |
| title_sort | optimization of spatio temporal ozone o3 pollution modeling using an ensemble machine model learning with a swarm based metaheuristic algorithm |
| topic | Ozone (O3) pollution Spatio-temporal modelling Ensemble machine learning Big data Public health |
| url | http://www.sciencedirect.com/science/article/pii/S0147651325011091 |
| work_keys_str_mv | AT seyedvahidrazavitermeh optimizationofspatiotemporalozoneo3pollutionmodelingusinganensemblemachinemodellearningwithaswarmbasedmetaheuristicalgorithm AT abolghasemsadeghiniaraki optimizationofspatiotemporalozoneo3pollutionmodelingusinganensemblemachinemodellearningwithaswarmbasedmetaheuristicalgorithm AT arminsorooshian optimizationofspatiotemporalozoneo3pollutionmodelingusinganensemblemachinemodellearningwithaswarmbasedmetaheuristicalgorithm AT lingboliu optimizationofspatiotemporalozoneo3pollutionmodelingusinganensemblemachinemodellearningwithaswarmbasedmetaheuristicalgorithm AT shumingbao optimizationofspatiotemporalozoneo3pollutionmodelingusinganensemblemachinemodellearningwithaswarmbasedmetaheuristicalgorithm AT soomichoi optimizationofspatiotemporalozoneo3pollutionmodelingusinganensemblemachinemodellearningwithaswarmbasedmetaheuristicalgorithm |