maryam cheraghian

Longshore sediment transport is considered as one of the most important and influential factors in the functioning of coastal areas. Forecasting and determining the rate of this parameter along the coast and in the vicinity of coastal structures is one the most important for shoreline management during any construction and coastal management mission. This study aims to put different pieces of knowledge together, including field measurements, neural networks, and numerical modelling to obtain a more realistic estimation of the LST rate along the undeveloped Makran Coastline. The focus of this paper is mostly on accurate wave and sediment transport modelling, verified against available field data and morphological evidence. A neural network for the correction of wave data and a numerical model of Mike21 is applied for simulating the transportation process of sediments along the Makran Coastline. Zarabad port on the coast of Makran has selected the case study of this research. The results of this study showed that the observed data are not in good agreement with the output data of the model (except wave height) and need to be modified with the neural network and considering the effective parameters such as the different conditions of the waves in the monsoon and non-monsoon seasons in the Neural network. Neural network can help a lot in improving and correcting data. The results also showed that the sediment transport process occurs in different directions and depending on the wave height at depths more than 4m and at greater depths the sediment transport is insignificant, also, wave force in the region is able to transfer approximately 235000 m3 / year sediment.