Air Pollution Modelling by Machine Learning Methods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00548678" target="_blank" >RIV/67985807:_____/21:00548678 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.3390/modelling2040035" target="_blank" >http://dx.doi.org/10.3390/modelling2040035</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/modelling2040035" target="_blank" >10.3390/modelling2040035</a>

Result language

angličtina

Original language name

Air Pollution Modelling by Machine Learning Methods

Original language description

Precise environmental modelling of pollutants distributions represents a key factor for addresing the issue of urban air pollution. Nowadays, urban air pollution monitoring is primarily carried out by employing sparse networks of spatially distributed fixed stations. The work in this paper aims at improving the situation by utilizing machine learning models to process the outputs of multi-sensor devices that are small, cheap, albeit less reliable, thus a massive urban deployment of those devices is possible. The main contribution of the paper is the design of a mathematical model providing sensor fusion to extract the information and transform it into the desired pollutant concentrations. Multi-sensor outputs are used as input information for a particular machine learning model trained to produce the CO, NO2, and NOx concentration estimates. Several state-of-the-art machine learning methods, including original algorithms proposed by the authors, are utilized in this study: kernel methods, regularization networks, regularization networks with composite kernels, and deep neural networks. All methods are augmented with a proper hyper-parameter search to achieve the optimal performance for each model. All the methods considered achieved vital results, deep neural networks exhibited the best generalization ability, and regularization networks with product kernels achieved the best fitting of the training set.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-23827S" target="_blank" >GA18-23827S: Capabilities and limitations of shallow and deep networks</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Modelling

ISSN

2673-3951

e-ISSN

2673-3951

Volume of the periodical

2

Issue of the periodical within the volume

4

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

659-674

UT code for WoS article

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EID of the result in the Scopus database

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