Machine 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…

By the Truvace deskOctober 3, 20262 min read1 sourcePeer-reviewedTRV-2026-1258· cite & verify

Machine Learning Outperforms Deep Learning for Atmospheric Attenuation Prediction in Free-Space Optical Communications under Iraqi Weather Conditions — GOPEX(galileo optical experiment) by NASA/JPL. Public domain

In brief

A study evaluated 15 machine learning, deep learning and hybrid models for predicting atmospheric attenuation in free-space optical communications under five Iraqi weather regimes. Using a 1500-sample synthetic dataset derived from physical propagation models, Random Forest achieved R2 0.9654 and RMSE 1.324 dB/km, outperforming the best deep learning model at R2 0.7766 and slightly edging the best hybrid at 0.9571.

The result matters because FSO links offer high bandwidth and license-free operation but degrade with scattering and absorption, especially in hot, dusty, foggy and rainy conditions. SHAP analysis highlighted dust concentration and visibility as dominant predictors, and RF also trained and inferred faster, though the authors note synthetic-data limits mean real atmospheric measurements are needed before operational deployment.

Main points

Synthetic dataset of 1500 samples built from physical propagation models for five regimes: clear sky, fog, rain, dust storms and snow.
Fifteen models tested: six machine learning, six deep learning including LSTM and CNN-LSTM, and three hybrid approaches.
Best deep learning R2 was 0.7766 versus RF 0.9654 and best hybrid 0.9571, with no significant difference between RF and best hybrid at p = 0.083.

The gain

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.

The rundown

Researchers generated 1500 synthetic samples using established physical propagation models to represent clear sky, fog, rain, dust storms and snow conditions relevant to Iraq.

They compared Random Forest, Extreme Gradient Boosting, LightGBM, SVR, Linear Regression, KNN against MLP, DNN, LSTM, 1D CNN, CNN-LSTM, Attention-based Network and three hybrids, using SHAP to rank features and statistical testing to compare top models.