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DOI	10.1109/EUROCON52738.2021.9535571
	Aerosol Optical Depth Forecast over Global Dust Belt Based on LSTM, CNN-LSTM, CONV-LSTM and FFT Algorithms
	Daoud, Nour; Eltahan, Mohamed; Elhennawi, Ahmed
通讯作者	Daoud, N (corresponding author)，Ain Shams Univ, Comp Engn Dept, Cairo, Egypt.
会议名称	19th International Conference on Smart Technologies (IEEE EUROCON)
会议日期	JUL 06-08, 2021
会议地点	Lviv, UKRAINE
英文摘要	Aerosols are sources of the uncertainty in the global atmosphere and climate. They have many critical health, economic and social impacts. In this paper, we assess prediction of temporal monthly of Aerosol Optical Depth (AOD) over four dust sources within the global dust belt using three different algorithms. The three models are long-short term memory (LSTM), Convolutional neural networks-long-short term memory (CNN-LSTM) and Convolutional long-short term memory (ConvLSTM). Classical Fast Fourier Transform (FFT) algorithm for time series predication is compared to the three neural networks models. Grid search is used to find the optimal internal weights for the proposed neural network algorithms. The four dust sources are Eastern Libyan Desert, Saudi Arabia Peninsula, Indian subcontinent and China. Monthly temporal (2005-2021) AOD product from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis is selected for training and validation periods. The presented models for AOD predication show efficient performance and cheap solution from computational point of view. However, ConvLSTM algorthims shows the least RMSE within +/- 10%.
英文关键词	LSTM CNN-LSTM ConvLSTM Aerosol optical depth (AOD) Global dust sources Global dust belt
来源出版物	IEEE EUROCON 2021 - 19TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES
出版年	2021
ISBN	978-1-6654-3299-3
出版者	IEEE
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000728121700034
WOS关键词	SIMULATIONS ; PREDICTION ; RETRIEVAL ; MODEL
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/379065
作者单位	[Daoud, Nour] Ain Shams Univ, Comp Engn Dept, Cairo, Egypt; [Eltahan, Mohamed] Univ Cologne, Inst Geophys & Meteorol, Cologne, Germany; [Elhennawi, Ahmed] Deggendorf Inst Technol, Fac Comp Sci, Deggendorf, Germany
推荐引用方式 GB/T 7714	Daoud, Nour,Eltahan, Mohamed,Elhennawi, Ahmed. Aerosol Optical Depth Forecast over Global Dust Belt Based on LSTM, CNN-LSTM, CONV-LSTM and FFT Algorithms[C]:IEEE,2021.