Investigation of unsteady non-Darcy flow through rockfill material using Saint–Venant equations and particle swarm optimization (PSO) algorithm

Norouzi, Hadi; Bazargan, Jalal; Taheri, S. Mohammad-Reza; Karimipour, Ahmadreza

doi:10.1007/s00477-023-02469-2

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…The particle swarm algorithm is a swarm intelligence algorithm (Norouzi et al 2023) that uses the learning experience of each particle to continuously search for the best direction to explore and thus nd the optimal solution for a given range. Eq.…”

Section: Sequential Minimal Optimizationmentioning

confidence: 99%

Haze prediction research based on PSO-CPU-GPU heterogeneous parallel support vector regression machine

Liu

Zhao

Liu

et al. 2023

Preprint

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Human life and development are affected by air quality, which means it is significant to accurately estimate air quality utilizing science. Considering PM2.5 is an essential component of air pollution, accurate PM2.5 prediction is crucial to maintaining air quality, preserving human wellness, and advancing urbanization's sustainability and scientific evolution. In this investigation, we provide a Particle Swarm Optimization (PSO) and Central Processing Unit-Graphics Processing Unit (CPU-GPU) heterogeneous parallel Support vector regression (SVR) method to forecast the airborne PM2.5 content. The approach enhances computational effectiveness, accelerates solution times, and satisfies debugging and application requirements in realistic circumstances where the size and dimensionality of the pertinent data are increasing. by contrasting it with conventional support vector regression methods and conducting analysis of variance on various data sets. The simulation results demonstrate a rise of over fifty per cent in prediction speed with a high degree of accuracy for the CPU-GPU heterogeneous parallel support vector regression (PSO-CPU-GPU-SVR) model.

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Section: Sequential Minimal Optimizationmentioning

confidence: 99%

Haze prediction research based on PSO-CPU-GPU heterogeneous parallel support vector regression machine

Liu

Zhao

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Design and optimization of haze prediction model based on particle swarm optimization algorithm and graphics processor

Liu,

Zhao,

Liu

et al. 2024

Sci Rep

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With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM2.5) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM2.5 levels is essential. However, air quality forecasting currently faces challenges in processing vast data and enhancing model accuracy. Deep learning models are widely applied for their superior learning and fitting abilities in haze prediction. Yet, they are limited by optimization challenges, long training periods, high data quality needs, and a tendency towards overfitting. Furthermore, the complex internal structures and mechanisms of these models complicate the understanding of haze formation. In contrast, traditional Support Vector Regression (SVR) methods perform well with complex non-linear data but struggle with increased data volumes. To address this, we developed CUDA-based code to optimize SVR algorithm efficiency. We also combined SVR with Genetic Algorithms (GA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO) to identify the optimal haze prediction model. Our results demonstrate that the model combining intelligent algorithms with Central Processing Unit-raphics Processing Unit (CPU-GPU) heterogeneous parallel computing significantly outpaces the PSO-SVR model in training speed. It achieves a computation time that is 6.21–35.34 times faster. Compared to other models, the Particle Swarm Optimization-Central Processing Unit-Graphics Processing Unit-Support Vector Regression (PSO-CPU-GPU-SVR) model stands out in haze prediction, offering substantial speed improvements and enhanced stability and reliability while maintaining high accuracy. This breakthrough not only advances the efficiency and accuracy of haze prediction but also provides valuable insights for real-time air quality monitoring and decision-making.

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Investigation of unsteady non-Darcy flow through rockfill material using Saint–Venant equations and particle swarm optimization (PSO) algorithm

Cited by 2 publications

References 36 publications

Haze prediction research based on PSO-CPU-GPU heterogeneous parallel support vector regression machine

Haze prediction research based on PSO-CPU-GPU heterogeneous parallel support vector regression machine

Design and optimization of haze prediction model based on particle swarm optimization algorithm and graphics processor

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