Freeze prediction for specific locations using artificial neural networks

dc.contributor.author	Jain, A	en
dc.contributor.author	McClendon, RW	en
dc.contributor.author	Hoogenboom, G	en
dc.date.accessioned	2014-06-06T06:46:47Z
dc.date.available	2014-06-06T06:46:47Z
dc.date.issued	2006	en
dc.identifier.issn	21510032	en
dc.identifier.uri	http://62.217.125.90/xmlui/handle/123456789/3204
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-33947106062&partnerID=40&md5=baf7c507c4e164f8d2c969be9de29105	en
dc.subject	Artificial neural networks	en
dc.subject	Crop protection	en
dc.subject	Meteorological modeling	en
dc.subject	Temperature prediction	en
dc.subject.other	Crop protection	en
dc.subject.other	Meteorological modeling	en
dc.subject.other	Temperature prediction	en
dc.subject.other	Atmospheric humidity	en
dc.subject.other	Crops	en
dc.subject.other	Freezing	en
dc.subject.other	Rain	en
dc.subject.other	Solar radiation	en
dc.subject.other	Weather forecasting	en
dc.subject.other	Neural networks	en
dc.subject.other	air temperature	en
dc.subject.other	artificial neural network	en
dc.subject.other	crop production	en
dc.subject.other	decision support system	en
dc.subject.other	error analysis	en
dc.subject.other	frost	en
dc.subject.other	meteorology	en
dc.subject.other	prediction	en
dc.subject.other	relative humidity	en
dc.subject.other	weather forecasting	en
dc.subject.other	Farm Crops	en
dc.subject.other	Freezing	en
dc.subject.other	Humidity	en
dc.subject.other	Neural Networks	en
dc.subject.other	Rain	en
dc.subject.other	Sun Light	en
dc.subject.other	Fort Valley	en
dc.subject.other	Georgia	en
dc.subject.other	North America	en
dc.subject.other	United States	en
dc.subject.other	Alma	en
dc.title	Freeze prediction for specific locations using artificial neural networks	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.abstract	Artificial neural networks (ANNs) were developed to predict air temperature in 1 h increments from 1 to 12 h in the future. Weather data for model development and evaluation for three locations in Georgia (Fort Valley, Blairsville, and Alma) were obtained from the Georgia Automated Environmental Monitoring Network (AEMN). The data consisted of observations of meteorological variables such as air temperature, relative humidity, wind speed, rainfall, and solar radiation. The critical inputs for each model were determined by developing ANNs that used them in various input combinations and observing their effect on the accuracy of the ANN predictions. The results showed that of the meteorological variables considered, only rainfall was not useful in generating air temperature predictions. The optimal duration of prior data ranged from 2 h to 6 h, depending on the period of prediction. The mean absolute error (MAE) increased as the period of prediction got longer. The MAE of the evaluation dataset for predicting temperature 1 h in advance was 0.6°C for Fort Valley, 0.7°C for Blairsville, and 0.6°C for Alma. The corresponding MAE values for a 12 h prediction were 2.4°C, 3.0°C, and 2.6°C. Further efforts will be directed to developing general ANNs based on data from multiple locations. The availability of decision support systems that incorporate localized temperature predictions for use by fruit growers could have a positive impact on frost damage protection. © 2006 American Society of Agricultural and Biological Engineers.	en
heal.journalName	Transactions of the ASABE	en
dc.identifier.issue	6	en
dc.identifier.volume	49	en
dc.identifier.spage	1955	en
dc.identifier.epage	1962	en