| dc.contributor.author |
Chronopoulos, KI |
en |
| dc.contributor.author |
Tsiros, IX |
en |
| dc.contributor.author |
Dimopoulos, IF |
en |
| dc.contributor.author |
Alvertos, N |
en |
| dc.date.accessioned |
2014-06-06T06:48:21Z |
|
| dc.date.available |
2014-06-06T06:48:21Z |
|
| dc.date.issued |
2008 |
en |
| dc.identifier.issn |
10934529 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1080/10934520802507621 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/4098 |
|
| dc.subject |
Artificial neural networks |
en |
| dc.subject |
Environmental management |
en |
| dc.subject |
Estimation |
en |
| dc.subject |
Meteorological data |
en |
| dc.subject |
Meteorological stations |
en |
| dc.subject |
Model |
en |
| dc.subject |
Prediction |
en |
| dc.subject.other |
Atmospheric temperature |
en |
| dc.subject.other |
Backpropagation |
en |
| dc.subject.other |
Environmental management |
en |
| dc.subject.other |
Gas turbines |
en |
| dc.subject.other |
Image classification |
en |
| dc.subject.other |
Meteorology |
en |
| dc.subject.other |
Network management |
en |
| dc.subject.other |
Regression analysis |
en |
| dc.subject.other |
Vegetation |
en |
| dc.subject.other |
Air temperatures |
en |
| dc.subject.other |
Ann models |
en |
| dc.subject.other |
Artificial neural network models |
en |
| dc.subject.other |
Artificial neural networks |
en |
| dc.subject.other |
Data estimations |
en |
| dc.subject.other |
Mean absolute errors |
en |
| dc.subject.other |
Meteorological data |
en |
| dc.subject.other |
Meteorological datums |
en |
| dc.subject.other |
Meteorological stations |
en |
| dc.subject.other |
Mlr models |
en |
| dc.subject.other |
Model results |
en |
| dc.subject.other |
Multiple regression models |
en |
| dc.subject.other |
National forests |
en |
| dc.subject.other |
Prediction |
en |
| dc.subject.other |
Reference stations |
en |
| dc.subject.other |
Sparse networks |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
air temperature |
en |
| dc.subject.other |
article |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
meteorology |
en |
| dc.subject.other |
multiple regression |
en |
| dc.subject.other |
Air |
en |
| dc.subject.other |
Linear Models |
en |
| dc.subject.other |
Meteorology |
en |
| dc.subject.other |
Models, Theoretical |
en |
| dc.subject.other |
Neural Networks (Computer) |
en |
| dc.subject.other |
Temperature |
en |
| dc.title |
An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1080/10934520802507621 |
en |
| heal.publicationDate |
2008 |
en |
| heal.abstract |
In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located in southern Greece. Four meteorological stations were established in the canyon; the models were then applied to estimate air temperature values as a function of the corresponding values of one or more reference stations. The evaluation of the ANN model results showed that fair to very good air temperature estimations may be achieved depending on the number of the meteorological stations used as reference stations. In addition, the ANN model was found to have better performance than the MLR model: mean absolute error values were found to be in the range 0.82-1.72°C and 0.90-1.81°C, for the ANN and the MLR models, respectively. These results indicate that ANN models may provide advantages over more traditional models or methods for temperature and other data estimations in areas where meteorological stations are sparse; they may be adopted, therefore, as an important component in various environmental modeling and management studies. Copyright © Taylor & Francis Group, LLC. |
en |
| heal.journalName |
Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering |
en |
| dc.identifier.issue |
14 |
en |
| dc.identifier.volume |
43 |
en |
| dc.identifier.doi |
10.1080/10934520802507621 |
en |
| dc.identifier.spage |
1752 |
en |
| dc.identifier.epage |
1757 |
en |