An Evaporation Duct Height Prediction Model Based on a Long Short-Term Memory Neural Network

被引：28

作者：

Zhao, Wenpeng ^{[1
,2
]}

Zhao, Jun ^{[1
]}

Li, Jincai ^{[1
]}

Zhao, Dandan ^{[3
]}

Huang, Lilan ^{[1
,2
]}

Zhu, Junxing ^{[1
]}

Lu, Jingze ^{[1
,2
]}

Wang, Xiang ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Meteorol & Oceanol, Changsha 410073, Peoples R China

[2] Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China

[3] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Layer Phys & Atm, Beijing 100029, Peoples R China

来源：

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION | 2021年 / 69卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Predictive models; Data models; Ducts; Atmospheric modeling; Temperature measurement; Sea surface; Logic gates; Babin-Young-Carton (BYC) model; deep learning; evaporation duct; eXtreme Gradient Boosting (XGB) model; long short-term memory (LSTM); Naval-Postgraduate-School (NPS) model; BULK PARAMETERIZATION; VALIDATION; FLUXES; HEAT;

D O I：

10.1109/TAP.2021.3076478

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Evaporation ducts are a particular type of atmospheric stratification that frequently appears on the sea surface. The accurate and timely prediction of the evaporation duct height (EDH) is significant for the practical application of electromagnetic communication equipment. Due to the typical time-series (TS) characteristics of the measured evaporation duct data, we construct an EDH prediction model based on a long short-term memory network (LSTM-EDH model) for the first time. The experimental results show that the LSTM-EDH model's root-mean-square error (RMSE) is dramatically reduced and can achieve a better fit of the measured EDH compared with the Babin-Young-Carton (BYC), Naval-Postgraduate-School (NPS), and eXtreme Gradient Boosting (XGB) EDH models. Compared with the XGB model, the generalization ability is also greatly improved.

引用

页码：7795 / 7804

页数：10