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

## TL;DR

This paper demonstrates that a multilayer perceptron neural network can accurately and rapidly estimate electromagnetic duct heights in the marine atmospheric boundary layer from sparse data, enabling real-time applications.

## Contribution

It introduces a novel ML approach with specific architecture choices for inferring EM duct heights from limited measurements in MABL.

## Key findings

- ML predictions are fast enough for real-time use
- The approach effectively estimates duct heights from sparse data
- Model architecture and hyperparameters are systematically justified

## 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.