Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area

Benedito Chi Man Tam; Su Kit Tang; Alberto Cardoso

doi:10.1186/s40537-024-01043-z

Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area

Benedito Chi Man Tam, Su Kit Tang, Alberto Cardoso

Faculty of Applied Sciences

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

An adaptively formulated Multi-level Lag Scheme can significantly. Improve the training process efficiently, and can be applied in mostly ANN-type deep learning model, with a practical case of Air Quality Alert Service in a city of sub-tropical area.

Original language	English
Article number	3
Journal	Journal of Big Data
Volume	12
Issue number	1
DOIs	https://doi.org/10.1186/s40537-024-01043-z
Publication status	Published - Dec 2025

Keywords

Air quality forecasting
Deep learning
Multi-level lag scheme
Multivariate

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s40537-024-01043-z

Cite this

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title = "Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area",

abstract = "An adaptively formulated Multi-level Lag Scheme can significantly. Improve the training process efficiently, and can be applied in mostly ANN-type deep learning model, with a practical case of Air Quality Alert Service in a city of sub-tropical area.",

keywords = "Air quality forecasting, Deep learning, Multi-level lag scheme, Multivariate",

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AU - Tang, Su Kit

AU - Cardoso, Alberto

N1 - Publisher Copyright: © The Author(s) 2024.

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N2 - An adaptively formulated Multi-level Lag Scheme can significantly. Improve the training process efficiently, and can be applied in mostly ANN-type deep learning model, with a practical case of Air Quality Alert Service in a city of sub-tropical area.

AB - An adaptively formulated Multi-level Lag Scheme can significantly. Improve the training process efficiently, and can be applied in mostly ANN-type deep learning model, with a practical case of Air Quality Alert Service in a city of sub-tropical area.

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KW - Deep learning

KW - Multi-level lag scheme

KW - Multivariate

UR - https://www.scopus.com/pages/publications/85214101110

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DO - 10.1186/s40537-024-01043-z

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ER -

Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area

Abstract

Keywords

UN SDGs

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