[Full Picture] Discharge coefficient prediction of canal radial gate using neurocomputing models: an investigation of free and submerged flow scenarios

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Discharge coefficient prediction of canal radial gate using neurocomputing models: an investigation of free and submerged flow scenarios

Source: tandfonline.com

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Article summary:

1. This article investigates the use of neurocomputing models to predict the discharge coefficient of canal radial gates in both free and submerged flow scenarios.

2. The article describes the datasets used, as well as the machine learning models adopted in the literature.

3. Model development and results are discussed, along with potential directions for future research.

Article analysis:

The article is generally trustworthy and reliable, as it provides a comprehensive overview of the research background, empirical formulation, machine learning models adopted in the literature, research gap and motivation, research objectives, dataset description, applied machine learning and modeling development, modeling results and assessment, discussion and potential directions for future research. The authors have provided sufficient evidence to support their claims throughout the article.

However, there are some areas that could be improved upon. For example, there is no mention of possible risks associated with using neurocomputing models to predict discharge coefficients or any counterarguments that could be made against this approach. Additionally, there is no discussion of how these models could be used in practice or what implications they may have on engineering design decisions. Furthermore, while the authors provide a detailed description of their datasets and machine learning models adopted in the literature, they do not provide any information on how these datasets were collected or how these models were developed or tested for accuracy. Finally, while the authors discuss potential directions for future research at the end of their article, they do not provide any concrete recommendations or suggestions on how to move forward with this research topic.

Topics for further research:

Risk assessment of neurocomputing models Engineering design decisions and neurocomputing models Dataset collection methods for neurocomputing models Model development and testing for accuracy Practical applications of neurocomputing models Recommendations for future research in neurocomputing models