Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

Ding, Jin; Zhang, Guoping; Wang, Shudong; Xue, Bing; Yang, Jing; Gao, Jinbing; Wang, Kuoyin; Jiang, Ruijiao; Zhu, Xiaoxiang

doi:10.3390/atmos13010075

Cited by 13 publications

(13 citation statements)

References 62 publications

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“…In particular, the tree-based ML algorithms reduced the bias (MAE) of visibility prediction more than the other ML algorithms (vector-, deep learning-, and regularizationbased algorithms, such as kNN, SVR, ANN, multilayer perceptron, partial least squares regression, stochastic gradient descent regression, etc.) (Yu et al 2021;Ding et al 2022). Therefore, we constructed the visibility prediction model based on the XGB algorithm considering its high visibility prediction accuracy among the three tree-based ML algorithms.…”

Section: B Machine Learning Results For Training and Validation Setsmentioning

confidence: 99%

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

Kim

Belorid

Wan

2022

Weather and Forecasting

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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).

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Section: B Machine Learning Results For Training and Validation Setsmentioning

confidence: 99%

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

Kim

Belorid

Wan

2022

Weather and Forecasting

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“…Furthermore, there were also some limitations with respect to the preprocessing steps of the observational data. Thresholding the data at a specific visibility threshold, as is common in other studies Yu et al, 2021;Kim et al, 2022b;2022b;Ding et al, 2022), can eliminate valuable information regarding the time evolution of the fog formation. For these studies, this may have not been important because their goal is to achieve either binary classification of fog/no-fog or just predict visibility level.…”

Section: Discussionmentioning

confidence: 99%

“…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).…”

Section: Nowcasting With ML Regressionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Gultepe,

Wang,

Blomquist

et al. 2024

Front. Earth Sci.

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Introduction: This study presents the application of machine learning (ML) to evaluate marine fog visibility conditions and nowcasting of visibility based on the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island, northeast of Canada.Methods: The measurements were collected using instrumentation mounted on the Research Vessel Atlantic Condor. The collected meteorological parameters were: visibility (Vis), precipitation rate, air temperature, relative humidity with respect to water, pressure, wind speed, and direction. Using all variables, the droplet number concentration was used to qualitatively indicate and assess characteristics of the fog using the t-distributed stochastic neighbor embedding projection method (t-SNE), which clustered the data into groups. Following t-SNE analysis, a correlation heatmap was used to select relevant meteorological variables for visibility nowcasting, which were wind speed, relative humidity, and dew point depression. Prior to nowcasting, the input variables were preprocessed to generate additional time-lagged variables using a 120-minute lookback window in order to take advantage of the intrinsic time-varying features of the time series data. Nowcasting of Vis time series for lead times of 30 and 60 minutes was performed using the ML regression methods of support vector regression (SVR), least-squares gradient boosting (LSB), and deep learning at visibility thresholds of Vis < 1 km and < 10 km.Results: Vis nowcasting at the 60 min lead time was best with LSB and was significantly more skillful than persistence analysis. Specifically, using LSB the overall nowcasts at Vis 1 < km and Vis 10 < km were RMSE = 0.172 km and RMSE = 2.924 km, respectively. The nowcasting skill of SVR for dense fog (Vis ≤ 400 m) was significantly better than persistence at all Vis thresholds and lead times, even when it was less skillful than persistence at predicting high visibility.Discussion: Thus, ML techniques can significantly improve Vis prediction when either observations or modelbased accurate time-dependent variables are available. The results suggest that there is potential for future ML analysis that focuses on modeling the underlying factors of fog formation.

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“…When compared to a deterministic reference forecast, the ndings of the ensemble forecasts showed to be more accurate. Ding et al (2022) created an arti cial intelligence-based methodology to forecast 1-hour visibility at Chinese airports. The forecasted results are encouraging, but further study is required for its operational implementation.…”

Section: Introductionmentioning

confidence: 99%

Fog Forecast Model based on Machine Learning

Almeida,

França,

de Almeida

et al. 2023

Preprint

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This study introduces an innovative approach for fog forecasting based on Machine Learning (ML) algorithms. It involved utilizing eighteen years of surface and sounding meteorological data from Guarulhos International Airport and nearby Marte Airfield for training and testing ML models. Multiple categorical algorithms were trained and evaluated, with the top three models selected for further investigation. The results of the study highlight that the best-performing model, which is based on the Random Forest algorithm, can provide reasonably accurate predictions for the occurrence of fog. Specifically, it forecasts fog occurrence within a time window from 03 to 11 UTC with a reasonable degree of accuracy (Proportion Correct = 0.90 ± 0.03, Probability of detection = 0.96 ± 0.03, False Alarm Rate = 0.33 ± 0.01, Critical Success Index = 0.65 ± 0.02, and Bias = 1.43 ± 0.05). Additionally, the method indicates the most likely time for the onset and dissipation of fog events based on historical data. This research offers valuable insights into improving fog forecasting at the Guarulhos International Airport and demonstrates the potential of ML algorithms in enhancing predictive accuracy for weather-related events.

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Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

Cited by 13 publications

References 62 publications

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

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

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

Fog Forecast Model based on Machine Learning

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