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Kharisma, Adilaksa
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Computer Science & IT Electrical & Electronics Engineering Energy Environmental Science Materials Science & Nanotechnology

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Advancing Aviation Meteorology: Airport Visibility Prediction Using Random Forest Regressor On Integrated Metar Parameters Kharisma, Adilaksa; Fadhillah, Muhammad; Haryanto, Yosafat Donni
JIIF (Jurnal Ilmu dan Inovasi Fisika) Vol 9, No 2 (2025)
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jiif.v9i2.65464

Abstract

To provide accurate and reliable visibility information in support of aviation safety at Soekarno-Hatta International Airport, a visibility prediction system was developed using the Random Forest Regressor algorithm based on 2024 METAR data. Visibility is a critical parameter for flight safety, particularly under adverse weather conditions. The dataset includes wind direction and speed, temperature, dew point, air pressure, weather phenomena, and cloud parameters that were numerically encoded. After preprocessing and quality control, the data was input into a Random Forest model optimized using Grid Search. Evaluation results show strong predictive performance with an R² value of 0.8736, MAE of 607.45 m, and RMSE of 772.29 m. Feature importance analysis identified haze, temperature, and mist as the most influential factors affecting visibility. These findings demonstrate that integrating meteorological observational data with machine learning approaches can provide accurate visibility predictions to support aviation operational decision-making.
Advancing Aviation Meteorology Airport Visibility Prediction Using Random Forest Regressor on Integrated METAR Parameters Kharisma, Adilaksa; Fadhillah, Muhammad; Haryanto, Yosafat Donni
JIIF (Jurnal Ilmu dan Inovasi Fisika) Vol 9, No 2 (2025)
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jiif.v9i2.65479

Abstract

To provide accurate and reliable visibility information in support of aviation safety at Soekarno-Hatta International Airport, a visibility prediction system was developed using the Random Forest Regressor algorithm based on 2024 METAR data. Visibility is a critical parameter for flight safety, particularly under adverse weather conditions. The dataset includes wind direction and speed, temperature, dew point, air pressure, weather phenomena, and cloud parameters that were numerically encoded. After preprocessing and quality control, the data was input into a Random Forest model optimized using Grid Search. Evaluation results show strong predictive performance with an R² value of 0.8736, MAE of 607.45 m, and RMSE of 772.29 m. Feature importance analysis identified haze, temperature, and mist as the most influential factors affecting visibility. These findings demonstrate that integrating meteorological observational data with machine learning approaches can provide accurate visibility predictions to support aviation operational decision making.
Co-Authors Haryanto, Yosafat Donni Muhammad Fadhil Z, Rusdi Noor Rosa (p:43-50)