A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques

A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques

Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In t...

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Bibliographic Details
Main Authors: Syed Ahsin Ali Shah, Wajid Aziz, Majid Almaraashi, Malik Sajjad Ahmed Nadeem, Nazneen Habib, Seong-O Shim
Format: Article
Language:English
Published: AIMS Press 2021-04-01
Series:Mathematical Biosciences and Engineering
Subjects:
empirical mode decomposition
forecasting
hybrid forecasting model
machine learning algorithms
particulate matter
Online Access:http://www.aimspress.com/article/doi/10.3934/mbe.2021104?viewType=HTML

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http://www.aimspress.com/article/doi/10.3934/mbe.2021104?viewType=HTML

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