Time series characterization and prediction of ambient PM2.5 concentrations in India:  A deep learning approach

dc.contributor.guide	Prakash, Amit
dc.coverage.spatial
dc.creator.researcher	Goswami, Pranjol
dc.date.accessioned	2025-03-04T04:55:29Z
dc.date.available	2025-03-04T04:55:29Z
dc.date.awarded	2024
dc.date.completed	2023
dc.date.registered	2018
dc.description.abstract	Available.
dc.description.note
dc.format.accompanyingmaterial	DVD
dc.format.dimensions
dc.format.extent	137 p.
dc.identifier.researcherid	0000-0001-6289-8811
dc.identifier.uri	http://hdl.handle.net/10603/625506
dc.language	English
dc.publisher.institution	Department of Environmental Science
dc.publisher.place	Tezpur
dc.publisher.university	Tezpur University
dc.relation
dc.rights	university
dc.source.university	University
dc.subject.keyword	3D convolution neural network
dc.subject.keyword	Bidirectional LSTM
dc.subject.keyword	Convolutional LSTM
dc.subject.keyword	Ecology and Environment
dc.subject.keyword	Encoder-decoder
dc.subject.keyword	Environmental Sciences
dc.subject.keyword	Hybrid deep learning model
dc.subject.keyword	Life Sciences
dc.subject.keyword	LSTM
dc.subject.keyword	SNR
dc.title	Time series characterization and prediction of ambient PM2.5 concentrations in India: A deep learning approach
dc.title.alternative
dc.type.degree	Ph.D.