| dc.contributor.author |
Smith, BA |
en |
| dc.contributor.author |
Hoogenboom, G |
en |
| dc.contributor.author |
McClendon, RW |
en |
| dc.date.accessioned |
2014-06-06T06:48:55Z |
|
| dc.date.available |
2014-06-06T06:48:55Z |
|
| dc.date.issued |
2009 |
en |
| dc.identifier.issn |
01681699 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.compag.2009.04.003 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/4331 |
|
| dc.subject |
Artificial intelligence |
en |
| dc.subject |
Frost protection |
en |
| dc.subject |
Fruit crops |
en |
| dc.subject |
Neural network |
en |
| dc.subject |
Temperature prediction |
en |
| dc.subject |
Vegetable crops |
en |
| dc.subject.other |
Air temperature |
en |
| dc.subject.other |
Artificial Neural Network |
en |
| dc.subject.other |
Artificial neural networks |
en |
| dc.subject.other |
Cloud cover |
en |
| dc.subject.other |
Decision support tools |
en |
| dc.subject.other |
Ensemble techniques |
en |
| dc.subject.other |
Environmental Monitoring |
en |
| dc.subject.other |
Extreme temperatures |
en |
| dc.subject.other |
Fruit crops |
en |
| dc.subject.other |
Georgia |
en |
| dc.subject.other |
Graphical analysis |
en |
| dc.subject.other |
Mean absolute error |
en |
| dc.subject.other |
Near real-time datum |
en |
| dc.subject.other |
Potential loss |
en |
| dc.subject.other |
Prediction accuracy |
en |
| dc.subject.other |
Prediction errors |
en |
| dc.subject.other |
Prediction horizon |
en |
| dc.subject.other |
Temperature prediction |
en |
| dc.subject.other |
Vegetable crops |
en |
| dc.subject.other |
Weather data |
en |
| dc.subject.other |
Winter months |
en |
| dc.subject.other |
Atmospheric temperature |
en |
| dc.subject.other |
Backpropagation |
en |
| dc.subject.other |
Crops |
en |
| dc.subject.other |
Decision support systems |
en |
| dc.subject.other |
Forecasting |
en |
| dc.subject.other |
Frost protection |
en |
| dc.subject.other |
Fruits |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
accuracy assessment |
en |
| dc.subject.other |
agricultural technology |
en |
| dc.subject.other |
air temperature |
en |
| dc.subject.other |
artificial intelligence |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
cloud cover |
en |
| dc.subject.other |
decision support system |
en |
| dc.subject.other |
environmental monitoring |
en |
| dc.subject.other |
frost |
en |
| dc.subject.other |
fruit |
en |
| dc.subject.other |
graphical method |
en |
| dc.subject.other |
horticulture |
en |
| dc.subject.other |
real time |
en |
| dc.subject.other |
North America |
en |
| dc.subject.other |
United States |
en |
| dc.title |
Artificial neural networks for automated year-round temperature prediction |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.compag.2009.04.003 |
en |
| heal.publicationDate |
2009 |
en |
| heal.abstract |
Crops and livestock in most of the southeastern United States are susceptible to potential losses due to extreme cold and heat. However, given suitable warning, agricultural and horticultural producers can mitigate the damage of extreme temperature events. To provide such a warning, air temperature prediction models are needed at horizons ranging from 1 to 12 h. The goal of this project was to explore the application of artificial neural networks (ANNs) for the prediction of air temperature during the entire year based on near real-time data. Ward-style ANNs were developed using detailed weather data collected by the Georgia Automated Environmental Monitoring Network (AEMN). The ANNs were able to provide predictions throughout the year, with a mean absolute error (MAE) of the year-round models that was less during the winter months than the MAE of the models resulting from the application of previously developed winter-specific models. The prediction MAE for a year-round evaluation set ranged from 0.516 °C at the one-hour horizon to 1.873 °C at the twelve-hour horizon. A detailed graphical analysis of MAE by time-of-year and time-of-day was also performed. A tendency to over-predict temperatures during summer afternoons was associated with localized cloud cover during that period. The inclusion of rainfall as input to the model was also shown to improve prediction accuracy. In addition, two simple ensemble techniques were explored and neither parallel nor series aggregation was found to reduce prediction errors. When simulated over two extreme temperature events, the models were capable of rapidly adjusting predictions on the basis of new information. The final models were applied to prediction horizons of 1-12 h and deployed on the website of the Georgia AEMN (www.GeorgiaWeather.net) for use as a general, year-round decision support tool. © 2009 Elsevier B.V. All rights reserved. |
en |
| heal.journalName |
Computers and Electronics in Agriculture |
en |
| dc.identifier.issue |
1 |
en |
| dc.identifier.volume |
68 |
en |
| dc.identifier.doi |
10.1016/j.compag.2009.04.003 |
en |
| dc.identifier.spage |
52 |
en |
| dc.identifier.epage |
61 |
en |