Yan, X., and Mohammadian, A., 0000. Evolutionary prediction of the trajectory of a rosette momentum jet group in flowing currents.
This study proposes a new approach to predicting the trajectory of a rosette momentum jet group in flowing currents, using multigene genetic programming (MGGP), which is an evolutionary-based artificial intelligence (AI) technique. The MGGP algorithm is used to develop explicit mathematical models that predict the dimensionless coordinates of the jet centerline trajectory as functions of the jet-to-ambient velocity ratios, the Reynolds numbers, the dimensionless jet angle, and the dimensionless travel distance. Experimental data are used to train the models, and the optimal models are identified using the Pareto-optimal approach, based on a performance–complexity trade-off. The same data, and some additional unseen data, are used to assess the performances of the developed models. The results show that the MGGP predictions have a good match with both the training and testing experimental datasets. The best MGGP model is also found to be superior to the best single-gene genetic programming (SGGP) model. This study demonstrates the suitability and capability of the MGGP technique in developing models for predicting the trajectory of a rosette momentum jet group in flowing currents, which can be used in many applications in the field of coastal science and engineering, such as the design of coastal outfall systems and assessment of environmental impacts.