Dewpoint temperature prediction using artificial neural networks

dc.contributor.author	Shank, DB	en
dc.contributor.author	Hoogenboom, G	en
dc.contributor.author	McClendon, RW	en
dc.date.accessioned	2014-06-06T06:48:06Z
dc.date.available	2014-06-06T06:48:06Z
dc.date.issued	2008	en
dc.identifier.issn	15588424	en
dc.identifier.uri	http://dx.doi.org/10.1175/2007JAMC1693.1	en
dc.identifier.uri	http://62.217.125.90/xmlui/handle/123456789/3959
dc.subject.other	Animal cell culture	en
dc.subject.other	Animals	en
dc.subject.other	Atmospheric humidity	en
dc.subject.other	Atmospheric temperature	en
dc.subject.other	Backpropagation	en
dc.subject.other	Climatology	en
dc.subject.other	Drought	en
dc.subject.other	Forecasting	en
dc.subject.other	Hydrostatic pressure	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Mathematical models	en
dc.subject.other	Sun	en
dc.subject.other	Vapor pressure	en
dc.subject.other	Vapors	en
dc.subject.other	Water supply	en
dc.subject.other	Water vapor	en
dc.subject.other	Air temperatures	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Dewpoint temperatures	en
dc.subject.other	General models	en
dc.subject.other	Georgia	en
dc.subject.other	h-Based	en
dc.subject.other	Heat stresses	en
dc.subject.other	Heat waves	en
dc.subject.other	Hidden layers	en
dc.subject.other	Initial ranges	en
dc.subject.other	Input datums	en
dc.subject.other	Iterative searches	en
dc.subject.other	Lead times	en
dc.subject.other	Learning rates	en
dc.subject.other	Mean absolute errors	en
dc.subject.other	Meteorological variables	en
dc.subject.other	Relative humidities	en
dc.subject.other	United States	en
dc.subject.other	Weather datums	en
dc.subject.other	Wind speeds	en
dc.subject.other	Neural networks	en
dc.subject.other	artificial neural network	en
dc.subject.other	back propagation	en
dc.subject.other	dew point	en
dc.subject.other	estimation method	en
dc.subject.other	evapotranspiration	en
dc.subject.other	parameterization	en
dc.subject.other	prediction	en
dc.subject.other	relative humidity	en
dc.subject.other	solar radiation	en
dc.subject.other	vapor pressure	en
dc.subject.other	water vapor	en
dc.subject.other	weather forecasting	en
dc.subject.other	wind velocity	en
dc.subject.other	Georgia	en
dc.subject.other	North America	en
dc.subject.other	United States	en
dc.subject.other	Animalia	en
dc.title	Dewpoint temperature prediction using artificial neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1175/2007JAMC1693.1	en
heal.publicationDate	2008	en
heal.abstract	Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans. © 2008 American Meteorological Society.	en
heal.journalName	Journal of Applied Meteorology and Climatology	en
dc.identifier.issue	6	en
dc.identifier.volume	47	en
dc.identifier.doi	10.1175/2007JAMC1693.1	en
dc.identifier.spage	1757	en
dc.identifier.epage	1769	en