On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models
Air pollution, particularly fine (PM<sub>2.5</sub>) and coarse (PM<sub>10</sub>) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in...
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MDPI AG
2025-07-01
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| author | Youness El Mghouchi Mihaela Tinca Udristioiu |
| author_facet | Youness El Mghouchi Mihaela Tinca Udristioiu |
| author_sort | Youness El Mghouchi |
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| description | Air pollution, particularly fine (PM<sub>2.5</sub>) and coarse (PM<sub>10</sub>) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R<sup>2</sup> values of 0.85 for PM<sub>2.5</sub> and 0.89 for PM<sub>10</sub>, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting. |
| format | Article |
| id | doaj-art-ff9c802c68d543b6b567c3d487bf33e8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-ff9c802c68d543b6b567c3d487bf33e82025-08-20T03:36:35ZengMDPI AGApplied Sciences2076-34172025-07-011515825410.3390/app15158254On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based ModelsYouness El Mghouchi0Mihaela Tinca Udristioiu1Department of Energetics, École Nationale Supérieure d’Arts et Métiers—ENSAM, Moulay Ismail University, Meknes 15290, MoroccoDepartment of Physics, Faculty of Sciences, University of Craiova, 13 A.I. Cuza Street, 200585 Craiova, RomaniaAir pollution, particularly fine (PM<sub>2.5</sub>) and coarse (PM<sub>10</sub>) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R<sup>2</sup> values of 0.85 for PM<sub>2.5</sub> and 0.89 for PM<sub>10</sub>, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting.https://www.mdpi.com/2076-3417/15/15/8254air pollutionPMshybrid AImodelspredictionforecasting |
| spellingShingle | Youness El Mghouchi Mihaela Tinca Udristioiu On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models Applied Sciences air pollution PMs hybrid AI models prediction forecasting |
| title | On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models |
| title_full | On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models |
| title_fullStr | On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models |
| title_full_unstemmed | On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models |
| title_short | On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models |
| title_sort | on the prediction and forecasting of pms and air pollution an application of deep hybrid ai based models |
| topic | air pollution PMs hybrid AI models prediction forecasting |
| url | https://www.mdpi.com/2076-3417/15/15/8254 |
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