Civil Engineering Department Researcher Updates Understanding of Machine Learnin g (Developing improved machine learning methods to predict the flow characterist ics through vertical and horizontal transitions in open channels)
Civil Engineering Department Researcher Updates Understanding of Machine Learnin g (Developing improved machine learning methods to predict the flow characterist ics through vertical and horizontal transitions in open channels)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Mansour a, Egypt, by NewsRx correspondents, research stated, "ABSTRACT: Transitions in a n open channel refer to the change in flow behavior due to changes in the channe l geometry." Our news editors obtained a quote from the research from Civil Engineering Depar tment: "Determining flow characteristics through transitions is an important top ic as it is necessary to guarantee the ideal hydraulic performance of water stru ctures with low costs. This research focuses on the flow characteristics through vertical and horizontal transitions through experimental study and then utilizi ng machine learning to predict the flow characteristics. The proposed framework aims to develop both the cascade-forward artificial neural network (CFANN) model and the regression model to enhance the prediction of flow characteristics. The first model developed modifies the CFANN using dandelion optimizer (DO) to dete rmine the ideal CFANN configuration. The second model used gene expression progr amming to develop statistical equations. The obtained CFANN-DO model has proven high accuracy in predicting the flow rates at various water loads and speeds ach ieving a coefficient of determination of approximately 100% for tr aining data and 99.5% for testing data."
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