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Satellite remote sensing and deep learning for aerosols prediction
Satelitsko osmatranje i duboko učenje za predviđanje aerosola
dc.creator | Mirkov, Nikola | |
dc.creator | Radivojević, Dušan | |
dc.creator | Lazović, Ivan | |
dc.creator | Ramadani, Uzahir | |
dc.creator | Nikezić, Dušan | |
dc.date.accessioned | 2024-08-21T10:09:39Z | |
dc.date.available | 2024-08-21T10:09:39Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 0042-8469 | |
dc.identifier.uri | https://vinar.vin.bg.ac.rs/handle/123456789/13687 | |
dc.description.abstract | Introduction/purpose: The paper presents a new state-of-the-art method that involves NASA satellite imagery with the latest deep learning model for a spatiotemporal sequence forecasting problem. Satellite-retrieved aerosol information is very useful in many fields such as PM prediction or COVID-19 transmission. The input data set was MODAL2_E_AER_OD which presents global AOT for every 8 days from Terra/MODIS. The implemented machine learning algorithm was built with ConvLSTM2D layers in Keras. The obtained results were compared with the new CNN LSTM model. Methods: Computational methods of Machine Learning, Artificial Neural Networks, Deep Learning. Results: The results show global AOT prediction obtained using satellite digital imagery as an input. Conclusion: The results show that the ConvLSTM developed model could be used for global AOT prediction, as well as for PM and COVID-19 transmission. | en |
dc.description.abstract | Uvod: Izložena je unapređena metoda koja uključuje Nasine satelitske snimke sa najnovijim modelom dubokog učenja koji se odnosi na problem predviđanja prostornovremenskih signala. Informacija o aerosolima sa satelitskih snimaka je vrlo značajna za predviđanje disperzije čestica u atmosferi i prenosa virusa COVID-19. Ulazni podaci MODAL2_E_AER_OD predstavljaju globalni AOT za osam dana sa Terra/MODIS. Algoritam mašinskog učenja je sačinjen od kompozitnih neuronskih slojeva ConvLSTM2D u biblioteci Keras. Dobijeni rezultati su upoređeni sa novim modelom CNN LSTM. Metode: Proračunske metode mašinskog učenja, veštačke neuronske mreže, duboko učenje. Rezultati: Rezultati prikazuju globalno predviđanje optičke debljine aerosola sa digitalnim satelitskim snimcima koji su korišćeni kao ulazni podaci. Zaključak: Pokazano je da je razvijeni model ConvLSTM pogodan za globalno predviđanje atmo-sferske debljine aerosola, kao i za prenos atmosferskih čestica i virusa COVID-19. | sr |
dc.relation | Ministry of Education and Science and Technological Development of the Republic of Serbia | |
dc.rights | openAccess | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Military Technical Courier : Vojnotehnički glasnik | en |
dc.subject | aerosol optical thickness | en |
dc.subject | NASA Earth observations | en |
dc.subject | ConvLSTM2D | en |
dc.subject | COVID-19 | en |
dc.subject | particulate matter dispersion | en |
dc.subject | optička debljina aerosola | sr |
dc.subject | NASA Earth Observations | sr |
dc.subject | ConvLSTM2D | sr |
dc.subject | COVID-19 | sr |
dc.subject | disperzija čestica | sr |
dc.title | Satellite remote sensing and deep learning for aerosols prediction | en |
dc.title | Satelitsko osmatranje i duboko učenje za predviđanje aerosola | sr |
dc.type | article | |
dc.rights.license | BY | |
dc.citation.volume | 71 | |
dc.citation.issue | 1 | |
dc.citation.spage | 66 | |
dc.citation.epage | 83 | |
dc.identifier.doi | 10.5937/vojtehg71-40391 | |
dc.type.version | publishedVersion | |
dc.identifier.fulltext | http://vinar.vin.bg.ac.rs/bitstream/id/38194/0042-84692301066M.pdf |
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