Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks

Published:2024-07-04 Issue: Volume:30 Page:16
ISSN:
Container-title:ITISE 2024
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Author:

Gudelj Ivan¹,Lovrić Mario²^ORCID,Nyarko Emmanuel Karlo¹^ORCID

Affiliation:

1. Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia

2. Centre for Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/68/1/16/pdf

Reference24 articles.

1. Descriptive Analysis of PM2.5 and PM10 at Regionally Representative Locations during SJVAQS/AUSPEX;Chow;Atmospheric Environ.,1996

2. Annual and Diurnal Variations of Gaseous and Particulate Pollutants in 31 Provincial Capital Cities Based on In Situ Air Quality Monitoring Data from China National Environmental Monitoring Center;Zhao;Environ. Int.,2016

3. (2023, July 12). Austrian Government Data. Available online: https://www.umwelt.steiermark.at/cms/ziel/2060750/DE/.

4. Streamflow prediction using LASSO-FCM-DBN approach based on hydro-meteorological condition classification;Chu;J. Hydrol.,2019

5. Analysis of the driving factors of PM2.5 concentration in the air: A case study of the Yangtze River Delta, China;Xu;Ecol. Indic.,2020

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