Satellite remote sensing and deep learning for aerosols prediction - Mirkov, Nikola S.; Radivojević, Dušan S.; Lazović, Ivan M.; Ramadani, Uzahir R.; Nikezić, Dušan P. (Vojnotehnički glasnik / Military Technical Courier, 2023)
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 im plemented machine learning algorithm was built with ConvLSTM2D lay ers 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.
Keywords: aerosol optical thickness; NASA Earth observations; ConvL-STM2D; COVID-19; particulate matter dispersion