He, H. and Luo, H. (2009) Fark Forecasting Method Based on Support Vector Machine Pattern Recognition. Meteorological Science and Technology, 37, 149-151.

has been cited by the following article:

• TITLE: The Application of Deep Learning in Airport Visibility Forecast

  AUTHORS: Lei Zhu, Guodong Zhu, Lei Han, Nan Wang

  KEYWORDS: Deep Learning, Airport Visibility, Regression Prediction

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.7 No.3, July 25, 2017

  ABSTRACT: This paper uses Urumqi International Airport’s hourly observation from 2007 to 2016 and builds regression prediction model for airport visibility with deep learning method. From the results we can see: the absolute error of hourly visibility is 706 m. When the visibility ≤ 1000 m, the absolute error is 325 m, and this method can predict visibility’s trend. So we can use this method to provide the airport visibility’s objective forecast guidance products for aviation meteorological services in the future. In this paper, the Urumqi area is as the research object, to explore the depth of learning in the field of weather forecasting applications, providing a new visibility return forecast for weather forecast personnel so as to improve the visibility of the level of visibility to ensure the safe and stable operation of the airport.