Forecasting of Low Visibility Using Weather and Air Quality Data for Safe and Smooth Transportation Operation

Low visibility condition affects the safety and operation of road, sea, and air traffic, which causes disruption in transportation and sometimes leads to serious accidents. Therefore, forecasting of low visibility accurately is of utmost importance for transportation agencies and aviation authorities. The conventional approach of visibility forecasting using time-series weather data cannot forecast low visibility with high accuracy due to imbalanced and clipped visibility data used to build a forecasting model. To address this issue, we propose a new approach that combines forecasting and classification methods with discrete and kernel density estimation (KDE)-based weights to build forecasting models for multi-step ahead forecasting. We show the effectiveness of our proposed approach by performing experiments with four publicly available visibility datasets of weather and air quality data from the USA and Japan and evaluating in terms of forecasting errors and accuracy of forecasting of low visibility. Experimental results suggest that our proposed approach can improve RMSE and MAE in the low visibility range by respectively 29–54 % and 40–59 % and F-score of forecasting of low and high visibility by 0.20-0.26 on the average over existing technologies for 3–24 hour ahead forecasting, which implies that it is well-suited for forecasting of low visibility.