Air Quality Forecaster: Moving Window Based Neuro Models

Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/3665

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DC Field	Value	Language
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
Appears in Collections:	Department of Civil Engineering

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