Paper Info
Title
Lazy Learning based surrogate models for air quality planning.
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. 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.
Year
DOI
Venue
2016
10.1016/j.envsoft.2016.04.022
Environmental Modelling and Software
Keywords
Field
DocType
Air quality,Surrogate models,Lazy Learning,Design of experiment
Data mining,Computer science,Decision support system,Lazy learning,Pollution,Air quality index,Deterministic system,Artificial intelligence,Air pollution,Artificial neural network,Machine learning,Design of experiments
Journal
Volume
Issue
ISSN
83
C
1364-8152
Citations 
PageRank 
References 
2
0.42
8
Authors
5
Name
Order
Citations
PageRank
Claudio Carnevale1518.60
Giovanna Finzi2406.95
Anna Pederzoli371.95
Enrico Turrini421.10
Marialuisa Volta5599.53