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Deep learning models for visibility forecasting using climatological data

Author

Listed:
  • Ortega, Luz C.
  • Otero, Luis Daniel
  • Solomon, Mitchell
  • Otero, Carlos E.
  • Fabregas, Aldo

Abstract

Low visibility conditions affect safety and traffic operations, leading to adverse scenarios that often result in serious accidents. Due to the complexity and variability associated with modeling weather variables, visibility forecasting remains a highly challenging task and a matter of significant interest for transportation agencies nationwide. Given that the literature on single-step visibility forecasting is very scarce, this study explores the use of deep learning models for single-step visibility forecasting using time series climatological data. Five different deep learning models were developed, trained, and tested using data from two weather stations located in the US state of Florida, which is one of the top states nationwide dealing with low visibility problems. The authors provide discussions of the models’ results and areas for future research.

Suggested Citation

  • Ortega, Luz C. & Otero, Luis Daniel & Solomon, Mitchell & Otero, Carlos E. & Fabregas, Aldo, 2023. "Deep learning models for visibility forecasting using climatological data," International Journal of Forecasting, Elsevier, vol. 39(2), pages 992-1004.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:2:p:992-1004
    DOI: 10.1016/j.ijforecast.2022.03.009
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    References listed on IDEAS

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    1. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
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    4. Koning, Alex J. & Franses, Philip Hans & Hibon, Michele & Stekler, H.O., 2005. "The M3 competition: Statistical tests of the results," International Journal of Forecasting, Elsevier, vol. 21(3), pages 397-409.
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