Machine Learning-based Short-term Visibility Classification for Wireless Optical Communication Systems Using METAR and Microwave-link Features at Bangkok Airports

Abstract

<jats:p>Rapid growth in connected devices, artificial intelligence applications, and the Internet of Things (IoT) is driving demand for ultra-high data rates, low latency, and energy-efficient communication infrastructure. Wireless optical communication (WOC), including free-space optical (FSO), is recognized as a disruptive technology for 6G and future-generation networks. However, atmospheric visibility critically affects WOC/FSO link availability, capacity, and reliability. This study proposes a machine learning (ML)-based low-visibility classification model that integrates Meteorological Aerodrome Reports (METARs) with microwave-link received-signal (Rx) features. Visibility below 6000 m is predicted at the 1 h, 3 h, and 6 h horizons using 18 months of data from Suvarnabhumi Airport (VTBS) and Don Mueang Airport (VTBD) in Bangkok, Thailand. Four ML algorithms, namely logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine (LGBM), are evaluated against persistence and Terminal Aerodrome Forecast (TAF) baselines. In a 100-round block-bootstrap evaluation, LGBM with METAR-Rx achieved the highest mean F1 scores at the 1 h and 3 h horizons, outperforming TAF by 28 and 20 percentage points at the 1 h horizon for VTBS and VTBD, respectively. SHAP and ablation analyses suggested that current visibility is the dominant predictor, while Rx features provide complementary information and improve F1 performance by approximately 1–4 percentage points. Seasonal analysis shows stronger cool-season performance, while rainy-season prediction remains challenging. Adding visibility-trend features further improves performance, with the best combined model achieving 1 h F1 scores of 0.7253 for VTBS and 0.6495 for VTBD. These findings indicate that integrating the METAR-Rx feature set can support short-term low-visibility classification.</jats:p>