Deep learning models for visibility forecasting using climatological data

Luz C. Ortega, Luis Daniel Otero, Mitchell Solomon, Carlos E. Otero and Aldo Fabregas

International Journal of Forecasting, 2023, vol. 39, issue 2, 992-1004

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.

Keywords: Weather forecasting; Neural network; Visibility forecast; Time series; Fog forecasting (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:992-1004

DOI: 10.1016/j.ijforecast.2022.03.009

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