Insights into Global Visibility Patterns: Spatiotemporal Distributions Revealed by Satellite Remote Sensing

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 468, P. 143069 - 143069

Published: Aug. 1, 2024

Language: Английский

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Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 5, 2024

Language: Английский

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Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

In this study, a range of machine learning (ML) models including random forest, adaptive boosting, gradient extreme light cat and stacked ensemble model, were employed to predict visibility at Bangkok airport. Furthermore, the impact influential factors was examined using Shapley method, an interpretable ML technique inspired by game theory-based approach. Air pollutant data from seven Pollution Control Department monitoring stations, visibility, meteorological Thai Meteorological Department's Weather station Airport, ERA5_LAND, ERA5 datasets, time-related dummy variables considered. Daytime ((here, 8–17 local time) screened for rainfall, developed prediction during dry season (November – April). The boosting model is identified as most effective individual with superior performance in three out four evaluation metrics (i.e., highest ρ, zero MB, second lowest ME, RMSE). However, SEM outperformed all both hourly daily time scales. seasonal mean standard deviation normalized are lower than those original indicating more influence meteorology emission reduction on improvement. analysis RH, PM_2.5, PM₁₀, day year, O₃ five important variables. At low relative humidity (RH), there no notable visibility. Nevertheless, beyond threshold, negative correlation between RH An inverse PM_2.5 PM₁₀ identified. Visibility negatively correlated moderate concentrations, diminishing very high concentrations. year Julian day) (JD) exhibits initial later positive association suggesting periodic effect. dependence values equal step size method understand effects, suggest effect hygroscopic growth aerosol Findings research feasibility employing techniques predicting comprehending influencing its fluctuations. Based above findings, certain policy–related implications, future work have been suggested.

Language: Английский

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Optimizing Nominal Current Output for Aeronautical Ground Lighting Using Machine Learning and Meteorological Data

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 100073 - 100085

Published: Jan. 1, 2024

Although there have been numerous studies on visibility prediction, insignificant conducted to predict nominal current output based visibility. Therefore, this study focuses optimizing at Subang Airport by employing artificial intelligence and meteorological data. The research leverages daily data enhance prediction address aeronautical ground lighting issues emphasizing the runway edge light. methodology involves a three-step modeling approach with Bayesian optimization. First, Gaussian Process Regression was utilized visibility, incorporating various parameters. Second, correction filter refines predictions, integrating models such as Trees, Support Vector Machines, Ensemble of Neural Networks, Regression. Finally, using error squared, generated from filter, time. Various machine learning models, including Decision Discriminant Analysis, Naïve Bayes Classifiers, Nearest Neighbor Network Classifiers were evaluated determine most effective model. Cross-fold validation 5-fold split ensures reliability precision algorithms. Performance metrics Mean Absolute Error, Squared Root R-squared used evaluate models. Results highlight stacked model Regression, accurate, achieving 96.2 % accuracy in predicting improving current. In conclusion, has introduced novel for light utilizing limited historical

Language: Английский

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Short-Term Fog Forecasting at Sofia Airport

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 167 - 177

Published: Nov. 15, 2024

Language: Английский

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Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts

Meteorological Applications, Journal Year: 2024, Volume and Issue: 31(6)

Published: Nov. 1, 2024

Abstract In our contemporary era, meteorological weather forecasts increasingly incorporate ensemble predictions of visibility—a parameter great importance in aviation, maritime navigation, and air quality assessment, with direct implications for public health. However, this variable falls short the predictive accuracy achieved other quantities issued by centers. Therefore, statistical post‐processing is recommended to enhance reliability predictions. By estimating distributions variables aid historical observations forecasts, one can achieve consistency between true Visibility observations, following recommendation World Meteorological Organization, are typically reported discrete values; hence, distribution quantity takes form a parametric law. Recent studies demonstrated that application classification algorithms successfully improve skill such forecasts; however, frequently emerging issue certain spatial and/or temporal dependencies could be lost marginals. Based on visibility European Centre Medium‐Range Weather Forecasts 30 locations Central Europe, we investigate whether inclusion Copernicus Atmosphere Monitoring Service (CAMS) same as an additional covariate methods it contributes successful integration dependence Our study confirms post‐processed substantially superior raw climatological predictions, utilization CAMS provides further significant enhancement both univariate multivariate setup. We also demonstrate significantly improves low events, which opens door aeronautical applications.

Language: Английский

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