A Random Forest-Based Method for Jet Concentration Prediction in Stratified Fluid Environments: Application and Comparison with Traditional Models

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@Article{yan:2025:Processes,

• author = "Xiaohui Yan and Xiaoxue Chi and Sidi Liu and Ziming Song and Liyan Lv",
• title = "A Random Forest-Based Method for Jet Concentration Prediction in Stratified Fluid Environments: Application and Comparison with Traditional Models",
• journal = "Processes",
• year = "2025",
• volume = "13",
• number = "3",
• pages = "Article No. 726",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2227-9717",
• URL = "https://www.mdpi.com/2227-9717/13/3/726",
• DOI = "doi:10.3390/pr13030726",
• abstract = "In engineering fluid dynamics and environmental science, jet concentration prediction is a complex multivariable problem influenced by multiple factors. The accurate simulation and prediction of jet behaviour are of significant theoretical and practical importance. However, traditional methods such as theoretical analysis and empirical formulas are applicable in simple or idealized environments and have limited applicability and accuracy in complex multilayered fluids. Computational fluid dynamics (CFD) can simulate more complex flow and concentration distributions but requires substantial computational resources. Therefore, this paper proposes a jet concentration prediction method based on a random forest model in a linear stratified environment. It uses OpenFOAM for flow field simulation to construct a comprehensive dataset, which is divided into training, validation, and test sets in a 6:2:2 ratio, and applies the random forest model for concentration prediction. By comparing it with support vector regression, linear regression, genetic programming, and Adaptive Boosting methods, the superiority of the random forest model in jet concentration prediction is validated. The results show that the overall R2 value of the random forest model reaches 0.99, which is closest to 1, with the lowest RMSE value. It can provide accurate predictions in a short time and has a strong generalisation capability. This study offers an efficient and precise alternative method for jet concentration prediction, maintaining a high prediction accuracy while reducing computational resource consumption, and providing strong support for practical engineering applications in fluid dynamics, chemical processes, environmental science, and related fields.",
• notes = "also known as \cite{pr13030726}",

}

Genetic Programming entries for Xiaohui Yan Xiaoxue Chi Sidi Liu Ziming Song Liyan Lv

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