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This study presents a hybrid machine learning framework that integrates video-based fog density estimation (using MVG modeling) with real-time atmospheric observations to bridge spatial and temporal data gaps. The framework includes a data preparation module and a training module combining LSTM and XGBoost. Experimentally, it achieves strong reconstruction performance with a test RMSE of 121.48\u00a0m and an R2 of 0.935, improving R2 by 5.67% over other LSTM hybrids. The optimized LSTM\u2013XGBoost model also outperforms both baseline models and unoptimized variants. These results confirm that the framework effectively utilizes video-derived fog density to dynamically calibrate visibility and deliver fast, accurate fog impact reconstruction.<\/jats:p>","DOI":"10.1007\/s11760-026-05144-5","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T19:01:52Z","timestamp":1773169312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An intelligent hybrid machine learning framework for low-visibility reconstruction for airport transportation"],"prefix":"10.1007","volume":"20","author":[{"given":"Fang-wei","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren-jie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"issue":"4","key":"5144_CR1","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1109\/JPHOT.2024.3410293","volume":"16","author":"M Li","year":"2024","unstructured":"Li, M., Wang, G.: A method combining mobile transmissometer and lidar for high precision measurement of visibility. 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