aceMachine Learning Outperforms Deep Learning for Atmospheric Attenuation Prediction in Free-Space Optical Communications under Iraqi Weather Conditions
Free-space optical communications systems offer high bandwidth, increased security and license-free operation but are highly affected by the performance degradation due to the atmospheric attenuation caused by scattering and absorption. The prediction of attenuation accuracy is even more important in Iraq where the environment is hot, dusty, foggy and rainy in a random fashion. The aim of this study is to assess the performance of machine learning, deep learning and hybrid modeling techniques for the prediction…
Random Forest predicted atmospheric attenuation for free-space optical links more accurately than deep learning models on a 1500-sample synthetic Iraqi weather dataset, with dust concentration and visibility as top drivers and faster training and inference.
Findings rest on synthetic data generated from physical models and have not been confirmed with field measurements, limiting operational applicability.
Evidence
- Peer-reviewedJournal of Visualized Experiments2026-10-01
How should this claim be treated?
Truvace Impact Record TRV-2026-1258, v1: “Machine Learning Outperforms Deep Learning for Atmospheric Attenuation Prediction in Free-Space Optical Communications under Iraqi Weather Conditions.” Truvace, 2026-10-03. /record/TRV-2026-1258 (accessed at citation time). sha256 d5986a5d3db5496a…
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