Using an Adaptive Neuro-Fuzzy Inference System to Predict Dilution Characteristics of Vertical Buoyant Jets Subjected to Lateral Confinement
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- @Article{zhao:2022:JMSE,
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author = "Yufeng Zhao and Junshi He and Xiaohui Yan and
Jianwei Liu",
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title = "Using an Adaptive Neuro-Fuzzy Inference System to
Predict Dilution Characteristics of Vertical Buoyant
Jets Subjected to Lateral Confinement",
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journal = "Journal of Marine Science and Engineering",
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year = "2022",
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volume = "10",
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number = "3",
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pages = "Article No. 439",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2077-1312",
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URL = "https://www.mdpi.com/2077-1312/10/3/439",
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DOI = "doi:10.3390/jmse10030439",
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abstract = "In order to predict the dilution characteristics of
vertical buoyant jets constrained by lateral
obstructions, we propose a new method based on a
commonly used machine learning algorithm: the adaptive
neuro-fuzzy inference system (ANFIS). By using
experimental data to train and test the ANFIS model,
this study shows that it had better performance than
commonly used empirical equations for laterally
confined jets and another artificial intelligence
technique—genetic programming. The RMSE values of
the ANFIS-based model were lower, and the R2 values
were higher, compared with those of the empirical
equation and genetic programming models. The reduction
in RMSE achieved by using ANFIS to replace the
empirical equations or genetic programming algorithm
exceeded 20percent. This research confirms that the
ANFIS technique has real potential in the development
of effective and accurate models that can be used to
estimate the dilution characteristics of a vertical
buoyant jet subjected to lateral confinement, providing
a new avenue for the prediction of dilution
characteristics using artificial intelligence
techniques, which can also be used for other effluent
mixing problems in marine systems.",
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notes = "also known as \cite{jmse10030439}",
- }
Genetic Programming entries for
Yufeng Zhao
Junshi He
Xiaohui Yan
Jianwei Liu
Citations