Predicting the surface currents of the Strait of Hormuz using artificial neural network

Sea current velocity measurement plays an important role in engineering design and measurements. Studies in the Persian Gulf and the Strait of Hormuz have conducted field studies or numerical modeling of flows in this region. In the present research, the surface currents of the Strait of Hormuz are predicted using artificial neural network approaches. In order to determine the model’s inputs, the eastern and western time series of surface currents of the strait are used, and the basins affecting the currents of this strait are identified using linear regression model. Then the neural network inputs are defined in two different cases. The first case of the time series of known basins is considered as the input of the neural network. In the other case, combinations of known time series are considered as the input of the neural network using the Jones schema. By comparing these two cases, it is concluded that the neural network model using the Jones scheme has a good performance in predicting the surface currents of this strait. In order to further investigate the neural network model, the flow data is divided into 16 different categories; so that in each category the difference between the minimum and maximum speed is 0.03 and the average of each category is considered as the output of the neural network. To determine the network inputs, the same is done for the two cases previously mentioned. In this case, the results show that the neural network model predicts surface currents with an accuracy of R = 0.85.