id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-2117	Lawal, Yusuf Babatunde; Owolawi, Pius Adewale ; Tu, Chunling ; Wyk, Etienne Van ; Ojo, Joseph Sunday 	Performance of advanced-deep learning algorithm for modeling and prediction of rain height over some selected locations in the tropics and sub-tropics zone for radio propagation applications	2024	18	.pdf	application/pdf	6847	370	57	Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.2117 © 2024 by the authors; licensee Learning Gate © 2024 by the author; licensee Learning Gate * Correspondence: lawalyb@tut.ac.za Performance of advanced-deep learning algorithm for modeling and prediction of rain height over some selected locations in the tropics and sub-tropics zone for radio propagation applications Yusuf Babatunde Lawal1*, Pius Adewale Owolawi2, Chunling Tu3, Etienne Van Wyk4, Joseph Sunday Ojo5 1,2,3Department of Computer Systems Engineering, Tshwane University of Technology, South Africa; lawalyb@tut.ac.za (Y. B. L.), owolawipa@tut.ac.za (P. A. O.), duc@tut.ac.za (C. T.) 4Faculty of Information and Communications Technology, Tshwane University of Technology, South Africa; vanwykea@tut.ac.za (E.V. W.) 5Department of Physics, Federal University of Technology, Akure, Nigeria; ojojs_74@futa.edu.ng (J. S. O.) Accurate prediction of attenuation requires continuous measurement and monitoring of rain-induced meteorological parameters, specifically rain rate and rain height, due to their spatio-temporal variations.	cache/easat-2117.pdf	txt/easat-2117.txt