The Comparison of Long Short-Term Memory Neural Network and Deep Forest for the Evaporation Duct Height Prediction-Reference-Cited by-同舟云学术

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The Comparison of Long Short-Term Memory Neural Network and Deep Forest for the Evaporation Duct Height Prediction

Published:2023-05 Issue:5 Volume:71 Page:4444-4450
ISSN:0018-926X
Container-title:IEEE Transactions on Antennas and Propagation
language:
Short-container-title:IEEE Trans. Antennas Propagat.

Author:

Liao Qixiang¹^ORCID,Mai Yanbo²^ORCID,Sheng Zheng¹,Wang Yuhui³,Ni Qingjian³,Zhou Shudao¹

Affiliation:

1. College of Meteorology and Oceanology, National University of Defense Technology, Changsha, China

2. College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

3. School of Computer Science and Engineering, Southeast University, Nanjing, China

Funder

Natural Science Foundation of Hunan Province

Hunan Outstanding Youth Fund Project

Research Projects of National University of Defense Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8/10116028/10068428.pdf?arnumber=10068428

Reference28 articles.

1. Modeling evaporation duct over sea with pseudo-refractivity and similarity theory;liu;Acta Electronica Sinica,2001

2. Determination of the evaporation duct height from standard meteorological data

3. Spectral Analysis of Gravity Waves in the Martian Thermosphere during Low Solar Activity Based on MAVEN/NGIMS Observations

4. Observations of Inertia Gravity Waves in the Western Pacific and Their Characteristic in the 2015/2016 Quasi‐Biennial Oscillation Disruption

5. LKB-Based Evaporation Duct Model Comparison with Buoy Data

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