Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models

Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models

Particulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), to analyze historical data from monitoring stations and predic...

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Bibliographic Details
Main Authors: Jesús Cáceres-Tello, José Javier Galán-Hernández
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:AppliedMath
Subjects:
particulate matter 2.5 μm
pollution prediction
data science
environmental policies
CRISP-DM
Prophet–long short-term memory (LSTM)
Online Access:https://www.mdpi.com/2673-9909/4/4/76
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