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    Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model
    (Elsevier, 2024) El Mghouchi, Youness; Udristioiu, Mihaela T.; Yildizhan, Hasan; Brancus, Mihaela
    Air quality forecasting is vital for managing and mitigating the adverse effects of air pollution on human health, crops, and the environment. This study aims to forecast daily time series of ozone and fine particulate matter (PM) concentrations using a meta-hybrid deep NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous inputs) model. Two datasets were utilised: (a) data on meteorological parameters (temperature, air pressure, relative humidity) and air pollutant concentrations (particulate matter, ozone, dioxide of carbon, volatile organic compounds, formaldehyde) provided by a sensor model A3 situated in the centre of Craiova city, and (b) data on wind direction, wind speed, and sunshine duration provided by the National Meteorological Administration. The data sets covered a time interval from December 10, 2020, to January 05, 2024. Initially, a statistical analysis was conducted to assess the correlation between variables. Results revealed that ozone concentration is primarily influenced by meteorological variables such as temperature (r = 0.79), sunshine duration (r = 0.55), and relative humidity (r = -0.48), and secondarily by air pollution indicators including VOC (r = -0.34), PM concentrations (r = -0.34), and CO2 (r = -0.3). In the subsequent stage, thirteen Machine Learning (ML) models were employed in conjunction with an integral feature selection (IFS) method to identify the best combinations of predictor variables for predicting ozone and PMs. Finally, a deep NARMAX model was developed to forecast the next periods of ozone and PMs based on the optimal combinations identified earlier. Results indicated the selection of sixty best models for ozone forecasting and four best models for PMs. The R-2 values surpassed 0.97 for ozone and exceeded 0.8 for PMs, demonstrating the efficacy of the forecasting approach.
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    Öğe
    Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning
    (Elsevier Sci Ltd, 2023) Udristioiu, Mihaela T.; EL Mghouchi, Youness; Yildizhan, Hasan
    This paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM1, PM2.5, PM10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (a). RMSE ranged between 0.65 and 1 & mu;g/m3, while a has values between 2.75 and 4.1 & mu;g/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square -Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and a vary between 8 and 9.1 & mu;g/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 & mu;g/m3, while the a ranged between 4.44 and 6.26 & mu;g/m3, further validating the precision of our forecasting model.
  • [ X ]
    Öğe
    Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning
    (Elsevier Sci Ltd, 2023) Udristioiu, Mihaela T.; EL Mghouchi, Youness; Yildizhan, Hasan
    This paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM1, PM2.5, PM10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (a). RMSE ranged between 0.65 and 1 & mu;g/m3, while a has values between 2.75 and 4.1 & mu;g/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square -Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and a vary between 8 and 9.1 & mu;g/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 & mu;g/m3, while the a ranged between 4.44 and 6.26 & mu;g/m3, further validating the precision of our forecasting model.