Air temperature prediction using artificial neural networks

Extreme air temperatures are responsible for economic losses in crops and livestock of agricultural producers. Freezing temperature during the growing season damage floral buds of fruit trees and extreme heat can wither plants and lead to heat stress in livestock. Suitable air temperature predictions can provide farmers and producers with valuable information when they face decisions regarding the use of mitigating technologies such as orchard heaters or irrigation. The research presented in this thesis developed artificial neural networks models for the prediction of air temperature up to 12 hours ahead. The predictions of the final models are now available year-round for all sites in the University of Georgias Automated Environmental Monitoring Network via the networks website, www.georgiaweather.net.