Lazy Learning based surrogate models for air quality planning. (September 2016)

• Record Type: Journal Article
• Authors: Carnevale, Claudio; Finzi, Giovanna; Pederzoli, Anna; Turrini, Enrico; Volta, Marialuisa
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 47
• Page End: 57
• Publication Date: 2016-09
• Subjects: Air quality -- Surrogate models -- Lazy Learning -- Design of experiment; 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.2016.04.022
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Air pollution in atmosphere derives from complex non-linear relationships, involving anthropogenic and biogenic precursor emissions. Due to this complexity, Decision Support Systems (DSSs) are important tools to help Environmental Authorities to control/improve air quality, reducing human and ecosystems pollution impacts. DSSs implementing cost-effective or multi-objective methodologies require fast air quality models, able to properly describe the relations between emissions and air quality indexes. These, namely surrogate models (SM), are identified processing deterministic model simulation data. In this work, the Lazy Learning technique has been applied to reproduce the relations linking precursor emissions and pollutant concentrations. Since computational time has to be minimized without losing precision and accuracy, tests aimed at reducing the amount of input data have been performed on a case study over Lombardia Region in Northern Italy. Highlights: The modellisation of PM10 concentration and emission precursors performed through simplified, computational efficient models based on Lazy Learning technique. Good performances both in terms of computational time and models evaluation. Comparison between Lazy Learning and Artificial Neural Network surrogate models.