A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information. (December 2018)

• Record Type: Journal Article
• Authors: Gómez-Losada, Álvaro; Asencio–Cortés, G.; Martínez–Álvarez, F.; Riquelme, J.C.
• Is Part Of: Environmental modelling & software. Volume 110(2018)
• Page Start: 52
• Page End: 61
• Publication Date: 2018-12
• Subjects: Time series -- Forecasting -- Data science -- Ozone concentration; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.08.013
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Surface ozone (O3 ) is considered an hazard to human health, affecting vegetation crops and ecosystems. Accurate time and location O3 forecasting can help to protect citizens to unhealthy exposures when high levels are expected. Usually, forecasting models use numerous O3 precursors as predictors, limiting the reproducibility of these models to the availability of such information from data providers. This study introduces a 24 h-ahead hourly O3 concentrations forecasting methodology based on bagging and ensemble learning, using just two predictors with lagged O3 concentrations. This methodology was applied on ten-year time series (2006–2015) from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to its forecasting performance and wide applicability, but also as benchmark methodology. Highlights: A new method to forecast hourly ozone concentrations is proposed. This method uses just lagged ozone concentrations as predictors. The reproducibility of this model does not depend on ozone precursors information. Bagging and ensembles are used in the model generation. Accuracy of the forecasting method is comparable to results found in literature.