This study was undertaken to produce local, short-term, artificial intelligence-based models that estimate the ozone level with special attention to the relationship between diurnal and nocturnal ozone variations of some primary pollutants and meteorological parameters in the city of Marrakesh, Morocco. Hourly data has been collected from the three air-quality monitoring stations in the city. This paper seeks to analyze the main factors that are associated with ozone formation, including the generation of different daytime and nighttime scenarios. The present work extends existing publications about the region by developing ozone prediction models from meteorological variables and primary pollutants. Several experiments were conducted to verify properties of the produced models, thus making it possible not only to describe but also to predict ozone pollution in this geographical area. The findings facilitate 48 hour forecasts that have root mean square errors as low as 20 g/m3. Our results highlight the importance of using such models for civil applications.

中文翻译：

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通过机器学习技术预测马拉喀什的地面臭氧

进行这项研究是为了产生局部，短期，基于人工智能的模型，这些模型可以估算臭氧水平，并特别关注摩洛哥马拉喀什市某些主要污染物的昼夜臭氧变化与气象参数之间的关系。每小时从城市的三个空气质量监测站收集数据。本文旨在分析与臭氧形成相关的主要因素，包括白天和夜晚场景的产生。通过根据气象变量和主要污染物开发臭氧预测模型，本工作扩展了该地区的现有出版物。进行了几次实验以验证所产生模型的特性，因此，不仅可以描述而且可以预测该地理区域中的臭氧污染。这些发现有助于进行48小时预测，其均方根误差低至20 g / m3。我们的结果突出了在民用中使用此类模型的重要性。