Short-Term Visibility Prediction Using Tree-Based Machine Learning Algorithms and Numerical Weather Prediction Data

Abstract

Accurate visibility prediction is imperative in the interests of human and environmental health. However, the existing numerical models for visibility prediction are characterized by low prediction accuracy and high computational cost. Thus, in this study, we predicted visibility using tree-based machine learning algorithms and numerical weather prediction data determined by the local data assimilation and prediction system (LDAPS) of Korea Meteorological Administration. We then evaluated the accuracy of visibility prediction for Seoul, South Korea, through a comparative analysis using observed visibility from the automated synoptic observing system. The visibility predicted by machine learning algorithm was compared with the visibility predicted by LDAPS. The LDAPS data employed to construct the visibility prediction model were divided into learning, validation, and test sets. The optimal machine learning algorithm for visibility prediction was determined using the learning and validation sets. In this study, the extreme gradient boosting (XGB) algorithm showed the highest accuracy for visibility prediction. Comparative results using the test sets revealed lower prediction error and higher correlation coefficient for visibility predicted by the XGB algorithm (bias: −0.62 km, MAE: 2.04 km, RMSE: 2.94 km, and R: 0.88) than for that predicted by LDAPS (bias: −0.32 km, MAE: 4.66 km, RMSE: 6.48 km, and R: 0.40). Moreover, the mean equitable threat score (ETS) also indicated higher prediction accuracy for visibility predicted by the XGB algorithm (ETS: 0.5−0.6 for visibility ranges) than for that predicted by LDAPS (ETS: 0.1−0.2).

Machine learning analysis and nowcasting of marine fog visibility using FATIMA Grand Banks campaign measurements

“…Gradient boosting using the least squares loss function (LSB) was the second implemented regression model due to its popularity and ability to achieve a high level of performance for many tasks, particularly for visibility classification and regression (Yu et al, 2021;Kim et al, 2022a;2022b;Ding et al, 2022;Vorndran et al, 2022). LSB successively fits many weaker regression trees on the residual error (Hastie et al, 2009).…”

“…Moreover, there was only one fog event in the prediction interval, which does not provide sufficient information regarding the variability of Vis. Other studies have attempted to predict the times series of visibility, not due to fog, but rather pollution Kim et al, 2022b;2022a;Ding et al, 2022), and at a coarse time resolution with very few low visibility events Yu et al, 2021).…”

Exploring Machine Learning Approaches to Precipitation Prediction: Post Processing of Daily Accumulated North American forecasts

“…A confusion matrix is a tabular representation indicating the number of true positive, true negative, false positive, and false negative predictions to assess the model's accuracy and error rates. Inspired by [68] and information from domain experts, we bin the predicted and observation data into 4 precipitation (PR) levels. These bins are labelled as following: Nil : P R < 0.20mm/day, Light: 0.20mm/day <= P R < 5mm/day, Moderate: 5mm/day <= P R < 20mm/day and Heavy: 20mm/day <= P R.…”