Indexado en

Texto completo

Licencia y uso

Citaciones

Altmetrics

Análisis de autorías institucional

Ordieres-Mere, JAutor (correspondencia)

Compartir

Predicting Ground Level Ozone in Marrakesh by Machine-Learning Techniques

Publicado en:Journal Of Environmental Informatics. 36 (2): 93-106 - 2020-12-01 36(2), DOI: 10.3808/jei.202000437

Autores: Ordieres-Mere, J; Ouarzazi, J; El Johra, B; Gong, B

Afiliaciones

Resumen

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 meteorologyical 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/m(3). Our results highlight the importance of using such models for civil applications.

Palabras clave