Analysis of Dynamic Changes in Sedimentation in the Coastal Area of Amir-Abad Port Using High-Resolution Satellite...

Sediment transport and shoreline changes causing shoreline morphodynamic evolution are key indicators of a coastal structure's operational continuity. To reduce the computational costs associated with sediment transport modelling tools, a novel procedure based on the combination of a support vector machine for image classification and a trained neural network to extrapolate the shore evolution is presented here. The current study focuses on the coastal area over the Amir-Abad port, using high-resolution satellite images. The real conditions of the study domain between 2004 and 2023 are analysed, with the aim of investigating changes in the shore area, shoreline position, and sediment appearance in the harbour basin. The measurements show that sediment accumulation increases by approximately 49,000 m²/y. A portion of the longshore sediment load is also trapped and deposited in the harbour basin, disrupting the normal operation of the port. Afterwards, satellite images were used to quantitatively analyse shoreline changes. A neural network is trained to predict the remaining time until the reservoir is filled (less than a decade), which is behind the west arm of the rubble-mound breakwaters. Harbour utility services will no longer be offered if actions are not taken to prevent sediment accumulation.