Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam

Article Environmental Sciences

Development and application of an automated air quality forecasting system based on machine learning

Huabing Ke et al.

Summary: An automated air quality forecasting system based on machine learning has been developed and applied, showing satisfactory performance in predicting pollutant concentrations and pollution levels, with good application prospects in the field of environmental meteorology.

SCIENCE OF THE TOTAL ENVIRONMENT (2022)

Add to Collection

Article Environmental Sciences

AI-based air quality PM2.5 forecasting models for developing countries: A case study of Ho Chi Minh City, Vietnam

Rajnish Rakholia et al.

Summary: This study analyzes and discusses the temporal characteristics of PM2.5 in Ho Chi Minh City, Vietnam, and develops AI-based PM2.5 forecasting models with state-of-the-art accuracy. These models will be deployed in the HealthyAir mobile app to warn citizens of air pollution issues in the city.

URBAN CLIMATE (2022)

Add to Collection

Review Computer Science, Artificial Intelligence

Intelligent modeling strategies for forecasting air quality time series: A review

Hui Liu et al.

Summary: This study introduced basic forecasting algorithms and commonly used auxiliary methods in air quality forecasting, summarized representative models to present the structures of efficient predictive models.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Review Green & Sustainable Science & Technology

A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance

Adil Masood et al.

Summary: Accurate air quality forecasting is crucial for pollution control and public health. Traditional forecasting techniques lack consistent accuracy, leading to increased interest in AI-based methods. This study reviews the most commonly used AI techniques for air pollution forecasting and identifies Deep Neural Networks as the best performing method.

JOURNAL OF CLEANER PRODUCTION (2021)

Add to Collection

Article Environmental Sciences

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Seyedeh Reyhaneh Shams et al.

Summary: This study compares the performance of Multiple Linear Regression (MLR) and Multi-layer perceptron (MLP) for predicting SO2 concentration in the air of Tehran, demonstrating that the MLP model is more accurate in predicting SO2 concentration. Factors influencing SO2 concentration identified by sensitivity analysis include time delay, park indicator, season/year, and total area parks. MLP model can be applied to support, analyze, and improve predicting air pollution and air quality management.

URBAN CLIMATE (2021)

Add to Collection

Review Environmental Sciences

Effects of Air Pollutants on Airway Diseases

Yun-Gi Lee et al.

Summary: Air pollutants, including toxic particles and gases, aggravate respiratory diseases such as asthma and COPD by triggering inflammation and dysfunction in the airways, leading to worsening symptoms. The mechanisms underlying these effects are still unclear, but air pollution is known to decrease quality of life and life expectancy while increasing the risk of hospitalization and morbidity associated with respiratory diseases.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2021)

Add to Collection

Article Multidisciplinary Sciences