Ozone (O3)

Accurately predicting tropospheric ozone (O$_3$) levels is essential for improving air quality in Tangier, given its significance as a major atmospheric pollutant.  In this study, we propose a hybrid forecasting approach that combines ARIMA-LSTM and Prophet-LSTM models to improve both the accuracy and robustness of ozone concentration predictions.  The method leverages the strengths of ARIMA and Prophet in capturing linear trends and seasonal variations, while leveraging the ability of LSTM networks to model nonlinear dynamics and long-term dependencies.  Comparative an

Ozone (O$_3$) from the troposphere is one of the substances that has a strong effect on air pollution in the city of Tanger.  Prediction of this pollutant can have positive improvements in air quality.  This paper presents a new approach combining deep-learning algorithms and the Holt–Winters method in order to detect pollutant peaks and obtain a more accurate forecasting model.  Given that LSTM is an extremely powerful algorithm, we hybridized with the Holt–Winters method to enhance the model.  Making use of multiple accuracy metrics, the models' efficiency is investig