Seminal Stacked Long Short-Term Memory (SS-LSTM) Model for Forecasting Particulate Matter (PM2.5 and PM10)

Seminal Stacked Long Short-Term Memory (SS-LSTM) Model for Forecasting Particulate Matter (PM2.5 and PM10)

With increased industrialization and urbanization, sustainable smart environments are becoming more concerned with particulate matter (PM) forecasts that are based on artificial intelligence (AI) techniques. The intercorrelation between multiple pollutant components and the extremely volatile PM pat...

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Bibliographic Details
Main Authors: Narendran Sobanapuram Muruganandam, Umamakeswari Arumugam
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Atmosphere
Subjects:
forecasting
air pollution
particulate matter
PM2.5
PM10
LSTM
Online Access:https://www.mdpi.com/2073-4433/13/10/1726

Internet

https://www.mdpi.com/2073-4433/13/10/1726

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