Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

Sit, Hilarie; Earls, Christopher J.

doi:10.1029/2019rs006798

Cited by 9 publications

(5 citation statements)

References 21 publications

(30 reference statements)

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“…Artificial intelligence originated in the 20th century and has been used in various industries, but it is seldom used in EDH prediction [27][28][29][30]. MLP is a kind of artificial neural network (ANN) with a forward structure [46][47][48] that maps a group of input vectors to a group of output vectors. The MLP consists of multiple layers and their neurons are fully connected to the next layer.…”

Section: Modeling Methodsmentioning

confidence: 99%

A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

2022

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The evaporation duct height (EDH) can reflect the main characteristics of the near-surface meteorological environment, which is essential for designing a communication system under this propagation mechanism. This study proposes an EDH prediction network with multi-layer perception (MLP). Further, we construct a multi-dimensional EDH prediction model (multilayer-MLP-EDH) for the first time by adding spatial and temporal "extra data" derived from the meteorological measurements. The experimental results show that: 1) compared with the naval-postgraduate-school (NPS) model, the root-mean-square error (RMSE) of the meteorological-MLP-EDH model is reduced to 2.15 m, and the percentage improvement reached 54.00 %; 2) spatial and temporal parameters can reduce the RMSE to 1.54 m with an improvement of 66.96 %; 3) the multilayer-MLP- EDH model can match measurements well at both large and small scales by attaching meteorological parameters at extra height, the error is further reduced to 1.05 m, with 77.51 % improvement compared with the NPS model. The proposed model can significantly improve the prediction accuracy of the EDH and has great potential to improve the communication quality, reliability, and efficiency of ducting in evaporation ducts.

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Section: Modeling Methodsmentioning

confidence: 99%

A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

2022

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“…Since the MABL is assumed stationary over the timeframe of minutes (Rogers, 1996), our predictions certainly qualify as being "real-time". Previously, we trained artificial neural networks to approximate a mapping between arrays of sparsely sampled propagation factors and duct heights (Sit and Earls, 2019). The small dataset, inherent in the duct height prediction, poses a challenge in training these networks.…”

Section: Timingmentioning

confidence: 99%

“…In prior work, the current authors show that artificial neural networks (ANNs) can accurately and efficiently predict duct height from sparsely measured EM propagation factors (Sit & Earls, 2019a). Similar to the above methods, the authors simulate coverage diagrams for duct heights of interest and utilize a series of sparse sampling schemes, that are consistent with practical deployment within bistatic contexts, to construct the data set needed for training and testing.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

Sit

Earls

2020

Radio Science

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We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistatic radars. We use GPR to calculate the posterior predictive distribution on the labels (i.e., duct height) from both noise-free and noise-contaminated array of propagation factors. For duct height inference from noise-contaminated propagation factors, we compare a naïve approach, utilizing one random sample from the input distribution (i.e., disregarding the input noise), with an inverse-variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a "ground truth" distribution, which is approximated using a large number of Monte-Carlo samples. The ability of GPR to yield accurate and fast duct height predictions using a few training examples indicates the suitability of the proposed method for real-time applications.

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“…Han [15] illustrated a method to predict the height of the evaporation duct using a recurrent neural network. Hilesit [16] demonstrated a method to characterize the parameters of the evaporation duct in the ocean boundary layer based on an artificial neural network. Han [17] outlined a cooperative inversion model of atmospheric duct parameters using ground-based GNSS occultation signals and a deep-learning network and established a weight loss function construction method.…”

Section: Introductionmentioning

confidence: 99%

A Classifying-Inversion Method of Offshore Atmospheric Duct Parameters Using AIS Data Based on Artificial Intelligence

Han

Zhang³

et al. 2022

Remote Sensing

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Atmospheric duct parameters inversion is an important aspect of microwave-band radar and communication system performance evaluation. AIS (Automatic Identification System) is one of the signal sources used for atmospheric duct parameters inversion. Before the inversion of atmospheric duct parameters, determining the type of atmospheric duct plays an important role in the inversion results, but the current inversion methods ignore this point. We outlined a classifying-inversion method of atmospheric duct parameters using AIS signals combined with artificial intelligence. The method consists of an atmospheric duct classification model and a parameter inversion model. The classification model judges the type of atmospheric duct, and the inversion model inverts the atmospheric duct parameters according to the type of atmospheric duct. Our findings demonstrated that the accuracy of the atmospheric duct classification model based on deep neural network (DNN) even exceeds 97%, and the atmospheric duct parameters inversion model has better inversion accuracy than that of the traditional method, thereby illustrating the effectiveness and accuracy of this novel method.

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Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

Cited by 9 publications

References 21 publications

A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

A Classifying-Inversion Method of Offshore Atmospheric Duct Parameters Using AIS Data Based on Artificial Intelligence

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