| dc.contributor.author |
Barai, Sudhir Kumar |
|
| dc.date.accessioned |
2021-11-27T04:13:28Z |
|
| dc.date.available |
2021-11-27T04:13:28Z |
|
| dc.date.issued |
2009 |
|
| dc.identifier.issn |
https://link.springer.com/chapter/10.1007/978-3-540-88079-0_14 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/3665 |
|
| dc.description.abstract |
The present paper aims to demonstrate neural network based air quality forecaster, which can work with limited number of data sets and are robust enough to handle air pollutant concentrations data and meteorological data. Performance of neural network models is reported using novel approach of moving window concept for data modelling. The performance of model is checked with reference to other research work and found to be encouraging. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
Springer |
en_US |
| dc.subject |
Civil Engineering |
en_US |
| dc.subject |
Artificial neural networks |
en_US |
| dc.subject |
Moving window modeling |
en_US |
| dc.subject |
Forecasting |
en_US |
| dc.subject |
Maidstone |
en_US |
| dc.title |
Air Quality Forecaster: Moving Window Based Neuro Models |
en_US |
| dc.type |
Book chapter |
en_US |