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

Abstract

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.

arXiv comment13 pages, 7 figures

References in corpus (2)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

Cited by in corpus (3)

• Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
• Deep Learning for Classifying and Characterizing Atmospheric Ducting within the Maritime Setting
• Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer