Visibility Prediction Based on Machine Learning Algorithms
Yu Zhang, Yangjun Wang, Yingqian Zhu, Lizhi Yang, Ge Lin, Luo Chun
Abstract
In this study, ground observation data were selected from January 2016 to January 2020. First, six machine learning methods were used to predict visibility. We verified the accuracy of the method with and without principal components analysis (PCA) by combining actual examples with the European Centre for Medium-Range Weather Forecast (ECMWF) data and National Centers for Environmental Prediction (NECP) data. The results show that PCA can improve visibility prediction. Neural networks have high accuracy in machine learning algorithms. The initial visibility data plays an important role in the visibility forecast and can effectively improve forecast accuracy.
Topics & Concepts
VisibilityComputer scienceArtificial neural networkMachine learningArtificial intelligencePrincipal component analysisAlgorithmWeather forecastingRange (aeronautics)Weather predictionMeteorologyData miningGeographyEngineeringAerospace engineeringMeteorological Phenomena and SimulationsUrban Heat Island MitigationWind and Air Flow Studies