Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Sustainable Cities and Society, Год журнала: 2024, Номер 109, С. 105537 - 105537

Опубликована: Май 18, 2024

Язык: Английский

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

Results in Engineering, Год журнала: 2024, Номер 22, С. 102305 - 102305

Опубликована: Май 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.

Язык: Английский

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Effect of characteristic parameters of air-bleeding/blowing interface on tunnel pressure waves in streamlined regions of high-speed trains: A numerical simulation study

Sustainable Cities and Society, Год журнала: 2024, Номер 102, С. 105222 - 105222

Опубликована: Янв. 18, 2024

Язык: Английский

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Numerical and machine learning based evaluation of ethylene glycol based hybrid nano-structured (TiO2-SWCNTs) fluid flow

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 19, 2025

A better mechanical resistance and improved thermal conductivity, as compared to mono nano-liquids, can be attained by the ethylene glycol-based hybrid nanofluids. These fluids have substantial uses in several engineering systems. The main focus, recent work, is assess dynamics of nano-structured fluid via computational (CFD) machine learning (ML) approaches. nano-composition single-walled carbon nanotubes (SWCNTs) titanium dioxide (TiO2) glycol causes mixture SWCNTs-TiO2/EG. CFD model, for simulation procedure, developed incorporating similarity coordinates governing partial differential equations. This model comprises a dimensionless system having prime parameters problem. numerical results are appraised means comparison between present existing results. levenberg marquardt (LM) technique powerful tool predict flow properties. complex correlations input properties interpreted with help well LM neural network. this work might provide basis design development high-performance heat exchangers management

Язык: Английский

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Machine Learning to speed up Computational Fluid Dynamics engineering simulations for built environments: A review

Building and Environment, Год журнала: 2024, Номер 267, С. 112229 - 112229

Опубликована: Ноя. 8, 2024

Язык: Английский

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Accelerating urban street canyon wind flow predictions with deep learning method

Building Simulation, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

Язык: Английский

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Development of Machine Learning-Aided Rapid CFD Prediction for Optimal Urban Wind Environment Design

Sustainable Cities and Society, Год журнала: 2025, Номер 121, С. 106208 - 106208

Опубликована: Фев. 16, 2025

Язык: Английский

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Numerical investigation of building gap effects on traffic pollutant dispersion in urban networks with intersecting streets

Atmospheric Pollution Research, Год журнала: 2025, Номер unknown, С. 102475 - 102475

Опубликована: Фев. 1, 2025

Язык: Английский

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Research on the Impact of Urban Built Environments on PM2.5 Pollution Based on Machine Learning Methods

Atmospheric Pollution Research, Год журнала: 2025, Номер unknown, С. 102503 - 102503

Опубликована: Март 1, 2025

Язык: Английский

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A prediction of urban boundary layer using Recurrent Neural Network and reduced order modeling

Building and Environment, Год журнала: 2025, Номер unknown, С. 112804 - 112804

Опубликована: Март 1, 2025

Язык: Английский

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