AIR QUALITY INDEX FORECASTING USING HYBRID NEURAL NETWORK MODEL WITH LSTM ON AQI SEQUENCES
This paper presents an approach to forecasting air pollution levels measured as Air Quality Index (AQI) metric using hybrid Long Short-Term Memory (LSTM) models. The pollution levels have been found to vary in a particular pattern that depends on both the overall climate or season as well as the hou...
Main Authors: | Shirshendu Roy, Pratyay Mukherjee |
---|---|
Format: | Article |
Language: | English |
Published: |
University of Kragujevac
2020-12-01
|
Series: | Proceedings on Engineering Sciences |
Subjects: | |
Online Access: | https://pesjournal.net/journal/v2-n4/10.pdf |
Similar Items
-
The trend of changes in air quality index (AQI) in Mashhad using GIS
by: mohammad Miri, et al.
Published: (2016-06-01) -
Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China
by: Shah Zaib, et al.
Published: (2022-02-01) -
Pre and Post Covid-19 Lockdown: How the AQI of Three Major Cities of Pakistan will Change?
by: Ume Laila, et al.
Published: (2020-12-01) -
A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data
by: Serdar Arslan
Published: (2022-06-01) -
Determination of Tehran air quality with emphasis on air quality index (AQI); 2008-2009
by: M. Farzadkia, et al.
Published: (2010-01-01)