Air Quality Forecaster: Moving Window Based Neuro Models

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dc.contributor.authorBarai, Sudhir Kumar
dc.date.accessioned2021-11-27T04:13:28Z
dc.date.available2021-11-27T04:13:28Z
dc.date.issued2009
dc.description.abstractThe 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.identifier.issnhttps://link.springer.com/chapter/10.1007/978-3-540-88079-0_14
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/3665
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectCivil Engineeringen_US
dc.subjectArtificial neural networksen_US
dc.subjectMoving window modelingen_US
dc.subjectForecastingen_US
dc.subjectMaidstoneen_US
dc.titleAir Quality Forecaster: Moving Window Based Neuro Modelsen_US
dc.typeBook chapteren_US

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