Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam

Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam

This study aims to predict fine particulate matter (PM2.5) pollution in Ho Chi Minh City, Vietnam, using autoregressive integrated moving average (ARIMA), linear regression (LR), random forest (RF), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and convolutional neural network (CNN)...

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Bibliographic Details
Main Authors: Tuyet Nam Thi Nguyen, Tan Dat Trinh, Pham Cung Le Thien Vu, Pham The Bao
Format: Article
Language:English
Published: Vilnius Gediminas Technical University 2024-11-01
Series:Journal of Environmental Engineering and Landscape Management
Subjects:
PM2.5
machine learning
ARIMA
univariate time series
Ho Chi Minh City
Online Access:https://transport.vilniustech.lt/index.php/JEELM/article/view/22361

Internet

https://transport.vilniustech.lt/index.php/JEELM/article/view/22361

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