Transformer-Based Model for Multi-Horizon Forecasting Ozone in Marrakech city, Morocco

Ozone is a major air pollutant that can cause respiratory problems and other health risks. It is formed when pollutants from cars, power plants, and other sources react in the presence of sunlight. Morocco is particularly vulnerable to ozone pollution due to its hot, dry climate. In recent years, there has been a growing interest in using machine learning to predict ozone pollution. Transformers are a type of neural network that have been shown to be effective in a variety of natural language processing tasks, and they have recently been applied to the problem of ozone prediction. In this paper, we present a transformer-based model for predicting ozone in Morocco. We use a dataset of historical ozone measurements and weather data to train our model. Our model is able to predict ozone with high accuracy, and it can be used to improve ozone management in Morocco.