Development of Hybrid ARIMA-LSTM and Prophet-LSTM Models for Air Quality Forecasting in the Tangier Region
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