Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža

Gudelj, Ivan

master's thesis

Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža

Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. urn:nbn:hr:200:096045

Gudelj, Ivan

Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Computer Engineering and Automation
Chair of Automation and Robotics

Cite this document

APA 6th Edition

Gudelj, I. (2024). Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:096045

MLA 8th Edition

Gudelj, Ivan. "Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:096045

Chicago 17th Edition

Harvard

Gudelj, I. (2024). 'Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 09 July 2024, https://urn.nsk.hr/urn:nbn:hr:200:096045

Vancouver

Gudelj I. Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2024 [cited 2024 July 09] Available at: https://urn.nsk.hr/urn:nbn:hr:200:096045

IEEE

I. Gudelj, "Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:200:096045

Cite this item: https://urn.nsk.hr/urn:nbn:hr:200:096045

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Metadata

Title	Modeliranje dnevnih koncentracije čestica u zrak pomoću konvolucijskih neuronskih mreža
Title (english)	Modeling daily concentrations of air particles using convolutional neural networks
Author	Ivan Gudelj
Mentor	Emmanuel Karlo Nyarko (mentor)
Mentor	Mario Lovrić (sumentor)
Committee member	Emmanuel Karlo Nyarko (predsjednik povjerenstva)
Committee member	Ratko Grbić (član povjerenstva)
Committee member	Ivan Vidović (član povjerenstva)
Granter	Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering, Computer Science and Information Technology Osijek (Department of Computer Engineering and Automation) (Chair of Automation and Robotics) Osijek
Defense date and country	2024-02-05, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing Artificial Intelligence
Abstract	Ovaj rad fokusira se na unapređenje predikcije dnevnih koncentracija čestica u zraku na urbanim mjernim mjestima korištenjem konvolucijskih neuronskih mreža (CNN). Predviđeno je da 1D CNN, u usporedbi s algoritmima poput Random Forests, ima veću moć predviđanja. Podaci za predikciju oblikovani su kao vremenske multivarijatne vremenske serije. Analiza podataka provodila se korištenjem Pythona i MATLAB-a, dok je testiranje parametara modela izvedeno u Pythonu kako bi se identificirali optimalni parametri i stvorio što precizniji model. Rezultati pokazuju da je CNN model nadmašio performanse drugih modela, izraženo kroz koeficijente determinacije i apsolutne pogreške. Prikazuje se kako se predviđanja CNN modela usklađuju s stvarnim podacima, a svi modeli ukazuju na mogućnosti dodatne optimizacije. Daljnja analiza koncentracije ozona (O3) ilustrira sezonske varijacije i ukazuje na važnost praćenja koncentracija štetnih tvari u zraku. Iako su rezultati obećavajući, naglašava se potreba za nastavkom praćenja i smanjenjem emisija kako bi se očuvala kvaliteta zraka, posebno u urbanim područjima. Ovo istraživanje ukazuje na uspješnost 1D CNN u predviđanju koncentracija čestica u zraku, pridonoseći novim saznanjima i potičući daljnja istraživanja na polju kvalitete zraka.
Abstract (english)	This study focuses on improving the prediction of daily air particle concentrations at urban monitoring sites using 1D convolutional neural networks (CNN). It is hypothesized that 1D CNN, compared to algorithms like Random Forests, possesses greater predictive power. The prediction data is structured as multivariate time series. Data analysis was conducted using Python and MATLAB, and model parameter testing was performed in Python to identify optimal parameters and create the most efficient model. Results indicate that the CNN model outperformed other models, as evidenced by coefficients of determination and absolute errors. The predictions of the CNN model align with actual data, with all models suggesting potential for further optimization. Further analysis of ozone (O3) concentrations illustrates seasonal variations and underscores the importance of monitoring harmful air pollutant concentrations. While the results are promising, there is a need for ongoing monitoring and emission reduction efforts to preserve air quality, especially in urban areas. This research highlights the success of 1D CNN in predicting air particle concentrations, contributing new insights and encouraging further research in the field of air quality.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:200:096045
Study programme	Title: Graduate University Study Programme in Computer Engineering Study programme type: university Study level: graduate Academic / professional title: sveučilišni/a magistar/magistra inženjer/inženjerka računarstva (sveučilišni/a magistar/magistra inženjer/inženjerka računarstva)
Type of resource	Text
File origin	Born digital
Access conditions	Open access
Terms of use
Repository	Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Created on	2024-02-08 13:17:12