Fine particulate matter concentrations forecasting using long short-term memory network and meteorological inputs

Fine particulate matter concentrations forecasting using long short-term memory network and meteorological inputs

BACKGROUND AND OBJECTIVES: In metropolitan settings, the requirement to travel and participate in everyday tasks exposes numerous individuals to the harmful effects of air pollutants, specifically particulate matter 2.5, which has the potential to impact their well-being. Developing precise forecast...

Full description

Saved in:
Bibliographic Details
Main Authors: T. Istiana, B. Kurniawan, S. Soekirno, A. Wihono, D.E. Nuryanto, B.A. Pertala, A. Sopaheluwakan
Format: Article
Language:English
Published: GJESM Publisher 2024-10-01
Series:Global Journal of Environmental Science and Management
Subjects:
air pollution
hybrid deep learning models
long short term memory (lstm)
fine particulate matter (pm2.5) forecasting
Online Access:https://www.gjesm.net/article_713740_1f8b5c8bb9a4b57db70bff90a7856e5c.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items