Optimal Stacking Identification for the Machine Learning Assisted Improvement of Air Quality Dispersion Modeling in Operation

Evangelos Bagkis (), Theodosios Kassandros (), Lasse Johansson (), Ari Karppinen () and Kostas Karatzas ()
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Evangelos Bagkis: Aristotle University of Thessaloniki
Theodosios Kassandros: Aristotle University of Thessaloniki
Lasse Johansson: Finnish Meteorological Institute
Ari Karppinen: Finnish Meteorological Institute
Kostas Karatzas: Aristotle University of Thessaloniki

A chapter in Advances and New Trends in Environmental Informatics 2023, 2024, pp 39-56 from Springer

Abstract: Abstract Air quality modeling plays a crucial role in understanding and predicting the dispersion of pollutants in the atmosphere, aiding in the development of effective strategies for mitigating the adverse impacts of air pollution. Traditional air quality modeling commonly relies on deterministic models that simulate pollutant transport, and dispersion based on physical and chemical principles leading to analytical numerical simulations towards the identification of pollutant concentrations in ambient air. However, these models often face challenges in accurately capturing the complex and dynamic nature of pollutant behavior due to uncertainties in emission inventories, meteorological conditions, and local-scale variations in terrain and land use. ENFUSER is a local scale air quality model that operates in the greater Helsinki area in Finland that successfully addresses most of the mentioned challenges. In previous research (Kassandros et al., Atmospheric Environment. 307:119818, 2023) we formalized a machine learning-based methodology to assist the operational ENFUSER dispersion model in estimating the coarse particle concentrations. Here, we continue this line of research and evaluate the genetic algorithm hybrid stacking with a novel validation procedure coined spatiotemporal cross validation. The development of the validation procedure was deemed necessary to simulate closely the operational requirements of ENFUSER. Furthermore, we introduce a fitness function based on robust statistics (median and standard deviation) that forces the predictions to follow the distribution of the reference stations. Results obtained using the greater Helsinki area (including Vantaa and Espoo) as a testbed suggest that the combination of ENFUSER with the proposed framework can provide estimations with higher confidence and improves the correlation from 0.61 to 0.71, the coefficient of determination from 0.34 to 0.50 and reduces the RMSE by 2.2 μg/m3.

Keywords: Spatiotemporal cross validation; Coarse particles; Dispersion; ENFUSER; Genetic algorithm hybrid stacking; Ensemble; Operational; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-46902-2_3

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