| dc.contributor.author |
Shank, DB |
en |
| dc.contributor.author |
McClendon, RW |
en |
| dc.contributor.author |
Paz, J |
en |
| dc.contributor.author |
Hoogenboom, G |
en |
| dc.date.accessioned |
2014-06-06T06:48:07Z |
|
| dc.date.available |
2014-06-06T06:48:07Z |
|
| dc.date.issued |
2008 |
en |
| dc.identifier.issn |
08839514 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1080/08839510802226785 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/3968 |
|
| dc.subject.other |
Artificial intelligence |
en |
| dc.subject.other |
Backpropagation |
en |
| dc.subject.other |
Electric fault location |
en |
| dc.subject.other |
Environmental engineering |
en |
| dc.subject.other |
Environmental protection |
en |
| dc.subject.other |
Errors |
en |
| dc.subject.other |
Forecasting |
en |
| dc.subject.other |
Lightning |
en |
| dc.subject.other |
Membership functions |
en |
| dc.subject.other |
Vegetation |
en |
| dc.subject.other |
ANN modeling |
en |
| dc.subject.other |
Artificial neural network models |
en |
| dc.subject.other |
Artificial neural networks |
en |
| dc.subject.other |
Comfort levels |
en |
| dc.subject.other |
Data sets |
en |
| dc.subject.other |
Dew-point temperature |
en |
| dc.subject.other |
Ensemble models |
en |
| dc.subject.other |
Environmental Monitoring |
en |
| dc.subject.other |
Fuzzy memberships |
en |
| dc.subject.other |
Georgia |
en |
| dc.subject.other |
Independent evaluation |
en |
| dc.subject.other |
Mean absolute errors |
en |
| dc.subject.other |
Meteorological variables |
en |
| dc.subject.other |
Seasonal modeling |
en |
| dc.subject.other |
Stopping criterion |
en |
| dc.subject.other |
Web site |
en |
| dc.subject.other |
Neural networks |
en |
| dc.title |
Ensemble artificial neural networks for prediction of dew point temperature |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1080/08839510802226785 |
en |
| heal.publicationDate |
2008 |
en |
| heal.abstract |
Dew point temperature is needed as an input to calculate various meteorological variables. In general, it contributes to human and animal comfort levels. The goal of this study was to develop artificial neural network (ANN) models for dew point temperature prediction to improve upon previous research. These improvements included optimizing the stopping criteria, comparing seasonal models to year-round models, and developing ensemble ANNs to blend the output of seasonal models. For an ANN trained with 100,000 patterns per epoch, the error was reduced using a 2000-pattern stopping dataset at an interval of 20 learning events to decide when to stop training. Seasonal ANN models were blended in an ensemble ANN with the weight of the member networks determined using a fuzzy membership-type function based on the day of year. These ensemble models were shown to produce lower errors than year-round, nonensemble models. The mean absolute errors (MAEs) of the final models evaluated with an independent evaluation dataset included 0.795°C for a 2-hour prediction, 1.485°C for a 6-hour prediction, and 2.146°C for a 12-hour prediction. The final model MAEs, when compared to the previous research, were reduced by 0.008°C, 0.081°C, and 0.135°C, respectively. It can be concluded that the methods used in this research were effective in more accurately predicting year-round dew point temperature. The ANN models for different prediction periods were sequenced to provide a 12-hour dew point temperature prediction system for implementation on the Georgia Automated Environmental Monitoring Network website (www.georgiaweather.net). Copyright © 2008 Taylor & Francis Group, LLC. |
en |
| heal.journalName |
Applied Artificial Intelligence |
en |
| dc.identifier.issue |
6 |
en |
| dc.identifier.volume |
22 |
en |
| dc.identifier.doi |
10.1080/08839510802226785 |
en |
| dc.identifier.spage |
523 |
en |
| dc.identifier.epage |
542 |
en |