Data-driven modeling of sluice gate flows using a convolutional neural network

Abstract

Predicting the flow field around sluice gates is essential for controlling water levels and discharges in open channels and rivers. Smooth particle hydrodynamics (SPH) models can satisfactorily reproduce such free-surface flows, but they typically require long computational time and extensive computational resources. In this work, we propose a convolutional neural network (CNN) to predict the flow field around a sluice gate. A validated SPH model is used to carry out extensive simulations, and the generated data set is used to train and test CNN-based models. The results demonstrated that the developed CNN can accurately reproduce sluice gate flows, with R2 values exceeding 90% and significantly reducing the computational costs. Furthermore, various traditional machine learning algorithms comprising adaptive neuro-fuzzy inference system, genetic programing, multigene genetic programing, and one-dimensional CNN were also evaluated, and a comparison of the results showed that the developed CNN performed better than the traditional data-driven algorithms in predicting sluice gate flows. Therefore, the proposed method is a promising tool for providing rapid prediction of the spatial distribution of flow fields near the sluice, and potentially for predicting other spatially distributed hydrologic variables.