Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102305 - 102305

Published: May 22, 2024

Air pollution in the environment is growing daily as a result of urbanization and population growth, which causes numerous health issues. Information about air quality environmental risks provided by pollutant data crucial for management. The use artificial neural network (ANN) approaches predicting pollutants reviewed this research. These methods are based on several forecast intervals, including hourly, daily, monthly ones. This study shows that ANN techniques contaminants more precisely than traditional methods. It has been discovered input parameters architecture-type algorithms used affect accuracy prediction models. therefore accurate reliable other empirical models because they can handle wide range meteorological parameters. Finally, research gap networks identified. review may inspire researchers to certain extent promote development intelligence prediction.

Language: Английский

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A novel hybrid model based on dual-layer decomposition and kernel density estimation for VOCs concentration forecasting considering influencing factors

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102439 - 102439

Published: Feb. 1, 2025

Language: Английский

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Real-time air quality prediction using traffic videos and machine learning

Transportation Research Part D Transport and Environment, Journal Year: 2025, Volume and Issue: 142, P. 104688 - 104688

Published: March 6, 2025

Language: Английский

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A systematic scrutiny of artificial intelligence-based air pollution prediction techniques, challenges, and viable solutions

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Oct. 9, 2024

Abstract Air is an essential human necessity, and inhaling filthy air poses a significant health risk. One of the most severe hazards to people’s pollution, appropriate precautions should be taken monitor anticipate its quality in advance. Among all countries, India decreasing daily, which matter concern department. Many studies use machine learning Deep methods predict atmospheric pollutant levels, prioritizing accuracy over interpretability. research confuse researchers readers about how proceed with further research. This paper aims give every detail considered pollutants brief techniques used, their advantages, challenges faced during prediction, leads better understanding before starting any related prediction. has given numerous prospective questions on pollution that piqued study’s interest. study discussed various deep optimization techniques. Despite techniques, concluded more datasets, variety suggestions would enhance interpretability while maintaining high for The purpose this review also reveal family neural network algorithms helped across globe pollutant(s).

Language: Английский

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Dynamic Modeling Under Temperature Variations for Sustainable Air Quality Solutions: PM2.5 and Negative Ion Interactions

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 70 - 70

Published: Dec. 26, 2024

Air pollution caused by fine particles known as PM2.5 is a significant health concern worldwide, contributing to illnesses like asthma, heart disease, and lung cancer. To address this issue, study focused on improving air purification systems using negative ions, which can attach these harmful help remove them from the air. This paper developed novel mathematical model based linear differential equations how interact with making it easier design more effective systems. The proposed was validated in small, controlled space, common urban pollutants such cigarette smoke, incense, coal, gasoline. These tests were conducted at different temperatures under two levels of ion generation. results showed that system could over 99% five minutes when low or moderate. However, higher temperatures, system’s performance dropped significantly. research goes beyond earlier studies examining temperature affects process, had not been fully explored before. Furthermore, approach aligns global sustainability goals promoting public health, reducing healthcare costs, providing scalable solutions for sustainable living.

Language: Английский

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