Using an Adaptive Neuro-Fuzzy Inference System to Predict Dilution Characteristics of Vertical Buoyant Jets Subjected to Lateral Confinement

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 AN-FIS 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 20%. 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 utilized for other effluent mixing problems in marine systems.