Several solution for assessing Particulate Matter concentrations

POHOATA, Alin; DUNEA, Daniel; LUNGU, Emil; SALISTEANU, Corneliu; NEDELCU, Otilia

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dc.contributor.author	POHOATA, Alin
dc.contributor.author	DUNEA, Daniel
dc.contributor.author	LUNGU, Emil
dc.contributor.author	SALISTEANU, Corneliu
dc.contributor.author	NEDELCU, Otilia
dc.date.accessioned	2020-11-03T16:04:27Z
dc.date.available	2020-11-03T16:04:27Z
dc.date.issued	2018
dc.identifier.citation	POHOATA, Alin, DUNEA, Daniel, LUNGU, Emil et al. Several solution for assessing Particulate Matter concentrations. In: CAIM 2018: The 26th Conference on Applied and Industrial Mathematics: Book of Abstracts, Technical University of Moldova, September 20-23, 2018. Chişinău: Bons Offices, 2018, p. 55.	en_US
dc.identifier.uri	http://repository.utm.md/handle/5014/11069
dc.description	Only Abstract	en_US
dc.description.abstract	Forecasting and analysis of the Particulate Matter (PM) concentrations is a subject of high interest for the public health. PM contains the inhalable particles that penetrate the thoracic region of the respiratory system determining numerous negative health effects particularly for younger children (0-10 years). We present in this article several methods of assessing the trends of PM concentrations, based on feedforward neural networks (FANN) combined with a wavelet decomposition of the time series values using smoothing filters to adjust the PM model outputs.	en_US
dc.language.iso	en	en_US
dc.publisher	Bons Offices	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Particulate Matter	en_US
dc.subject	methods	en_US
dc.subject	feedforward neural networks	en_US
dc.subject	PM concentrations	en_US
dc.title	Several solution for assessing Particulate Matter concentrations	en_US
dc.type	Article	en_US