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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An outlier detection framework for Air Quality Index prediction using linear and ensemble models

Decision Analytics Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100546 - 100546

Published: Jan. 1, 2025

Language: Английский

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Assessment of the dispersion of pollutants from automobile exhaust, taking into account relative humidity, pavement temperature, wind direction and speed, which varies depending on the time of day

International Communications in Heat and Mass Transfer, Journal Year: 2024, Volume and Issue: 159, P. 108140 - 108140

Published: Oct. 11, 2024

Language: Английский

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The Role of AI in Smart Mobility: A Comprehensive Survey

Electronics, Journal Year: 2025, Volume and Issue: 14(9), P. 1801 - 1801

Published: April 28, 2025

The advancement in Artificial Intelligence, particularly the application of deep learning methodologies, has allowed for implementation modern smart transportation systems, which are making driver experience increasingly reliable and safe. Unfortunately, a literature review revealed that no survey paper provides collective overview all machine applications involved systems. To fill this gap, discussion on role methodologies mobility aspects, highlighting their mutual dependencies. end, three key pillar areas considered: vehicles, planning, vehicle network security. In each area, subtasks commonly addressed by pointed out, state-of-the-art techniques reviewed, with final about advancements according to recent findings learning.

Language: Английский

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Predicting particulate matter (PM2.5) air pollution levels in Almaty city using machine learning techniques

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(4)

Published: April 28, 2025

Language: Английский

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Assessment of the height and the slope of the upper part of the protective barrier on the dispersion of pollutants from automobile exhausts by taking into account non-stationary external factors

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(2)

Published: Feb. 24, 2025

Language: Английский

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Assessing the Impact of a Low-Emission Zone on Air Quality Using Machine Learning Algorithms in a Business-As-Usual Scenario

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3582 - 3582

Published: April 16, 2025

The proliferation of low-emission zones (LEZs) across Europe is anticipated to accelerate in the coming years as a measure enhance air quality urban areas. Nevertheless, there lack standardized methodology evaluate their effectiveness, and some proposed strategies may not adequately address issues or overlook meteorological considerations. In this study, we employ three machine learning (ML) algorithms forecast NO2, PM10 PM2.5 concentrations Madrid 2022 (post-LEZ) based on data from period 2015–2018 (pre-LEZ) under business-as-usual scenario, accounting for seasonal factors. According models, reductions NO2 varied 29 35% contrast scenario without LEZ, which coherent with observed decrease traffic volume inside area limited by LEZ. However, no clear improvement was concentrations.

Language: Английский

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Autonomous vehicle pollution monitoring: An innovative solution for policy and environmental management

Transportation Research Part D Transport and Environment, Journal Year: 2024, Volume and Issue: 139, P. 104542 - 104542

Published: Dec. 13, 2024

Language: Английский

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