NDLI: On Modeling of Atmospheric Visibility Classification Forecast with Nonlinear Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Zai-Wen Wang Chao-Lin Zhang Chen Su Cong-Lan Cheng
Copyright Year	2009
Abstract	Based on the consecutive high temporal resolution data observed at the special automatic weather stations ROSA on Beijing airport highway during 2006-2007, the modeling of atmospheric visibility classification forecast model with the nonlinear Support Vector Machine method was discussed and evaluated in this paper. The evaluation result shows that the performance of the forecast model by the Support Vector Machine method was good. 40% of atmospheric visibility classification forecast is consistent with the observed data; more than 90% of the forecast classification errors is within one level (including equality). Moreover, in the future 3–48 h forecast the atmospheric visibility performed stably. The perfect forecast results verify that the Support Vector Machine method has strong capability of processing the nonlinear relationship between atmospheric visibility and meteorological factors.
Starting Page	240
Ending Page	244
File Size	953453
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.418
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Meteorological factors Atmospheric modeling atmospheric visibility Weather forecasting Predictive models Airports Support Vector Machine nonlinear Support vector machines Road transportation Support vector machine classification road weather information Road safety Meteorology
Content Type	Text
Resource Type	Article

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