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Deep learning based short-term total cloud cover forecasting.

Bandara, Ishara; Zhang, Li; Mistry, Kamlesh

Authors

Ishara Bandara

Li Zhang

Kamlesh Mistry



Abstract

In this research, we conduct deep learning based Total Cloud Cover (TCC) forecasting using satellite images. The proposed system employs the Otsu's method for cloud segmentation and Long Short-Term Memory (LSTM) variant models for TCC prediction. Specifically, a region-based Otsu's method is used to segment clouds from satellite images. A time-series dataset is generated using the TCC information extracted from each image in image sequences using a new feature extraction method. The generated time series data are subsequently used to train several LSTM variant models, i.e. LSTM, bi-directional LSTM and Convolutional Neural Network (CNN)-LSTM, for future TCC forecasting. Our approach achieves impressive average RMSE scores with multi-step forecasting, i.e. 0.0543 and 0.0823, with respect to both the first half of daytime and full daytime TCC forecasting on a given day, using the generated dataset.

Citation

BANDARA, I., ZHANG, L. and MISTRY, K. 2022. Deep learning based short-term total cloud cover forecasting. In Proceedings of the 2022 International joint conference on neural networks (IJCNN 2022), co-located with the 2022 conference proceedings of Institute of Electrical and Electronics Engineers (IEEE) World congress on computational intelligence (IEEE WCCI 2022), 18-23 July 2022, Padua, Italy. Piscataway: IEEE [online], article 9892773. Available from: https://doi.org/10.1109/IJCNN55064.2022.9892773

Conference Name 2022 International joint conference on neural networks (IJCNN 2022)
Conference Location Padua, Italy
Start Date Jul 18, 2022
End Date Jul 23, 2022
Acceptance Date Apr 26, 2022
Online Publication Date Sep 30, 2022
Publication Date Dec 31, 2022
Deposit Date Oct 6, 2022
Publicly Available Date Mar 29, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Series ISSN 2161-4407; 2161-4393
Book Title Proceedings of the 2022 International joint conference on neural networks (IJCNN 2022), co-located with the 2022 conference of Institute of Electrical and Electronics Engineers (IEEE) World congress on computational intelligence (IEEE WCCI 2022), 18-23 Ju
DOI https://doi.org/10.1109/IJCNN55064.2022.9892773
Keywords Long short-term memory; Deep learning; Total cloud cover; Time-series forecasting; Satellite imaging
Public URL https://rgu-repository.worktribe.com/output/1769264

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