Satelitsko osmatranje i duboko učenje za predviđanje aerosola
Sažetak
Uvod: Izložena je unapređena metoda koja uključuje Nasine satelitske snimke sa najnovijim modelom dubokog učenja koji se odnosi na problem predviđanja prostorno-vremenskih 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 Con-vLSTM2D 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.
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Sva prava zadržana (c) 2023 Nikola S. Mirkov, Dušan S. Radivojević, Ivan M. Lazović, Uzahir R. Ramadani, Dušan P. Nikezić
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