Identification and characteristics of Internal Waves (IWs) are widely used in a variety of industries, including shipbuilding, maritime, as well as oil and marine engineering. In this study, IWs have been detected using field measurement and satellite remote sensing in the Persian Gulf during the period 2000–2015. In order to identify the potential areas for IWs after vertical changes in the physical parameters such as temperature, salinity, and sigma-T (σT) resulted by field measurements, the areas where high stratifications of seawater were identified. Satellite images corresponding to the identified areas were then prepared and the required modifications were made to the images. IWs were then manifested by such images in different parts of the study area. For the first time, a comprehensive map of the occurrence of IWs has been prepared and presented using Landsat 7 and 8, Sentinel-1 and Sentinel-2 images. Then, spatio-temporal analysis of IWs indicates that the density of them decreases from the Strait of Hormuz in the east of the Persian Gulf to the Khowr-e-Musa estuary in the west. In addition, IWs mainly occur in the warm season, from early March to September and weaken or disappear in the cold season in the Persian Gulf.

Accurate prediction of significant wave height (SWH) in the Southern Ocean remains a critical challenge due to extreme weather conditions and limited observational data, impacting maritime safety and climate research. This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized with subtractive clustering for SWH forecasting, with its novelty lying in the integration of sequential time-lagged inputs and automated fuzzy rule generation to model complex nonlinear wave dynamics. The model leverages marine meteorological variables, including mean sea level pressure (MSLP), surface wind speed (SWS), and historical SWH records, using three consecutive time steps. High-quality data from MetOcean Spotter buoys and ERA5 reanalysis, collected at 3-hour intervals during 2019–2020, were divided into training (70%), testing (30%), and an independent prediction set (250 samples). Compared to conventional regression techniques such as neural networks, support vector machines, and Gaussian process regression, the ANFIS model demonstrated superior performance, achieving a root mean square error (RMSE) of 0.5142 m and a coefficient of determination (R²) of 0.8948. With efficient computational performance (65 milliseconds per prediction) and interpretable fuzzy rules, this model offers a robust tool for operational wave forecasting in polar regions, advancing maritime safety and enhancing understanding of ocean-atmosphere interactions.

Temperature, salinity, water-level and current speed around brine outflow of desalination site in the Southeastern Coast of Kish Island is investigated. The dominant current is east-west tidal current with a weak residual current which affects current field partially southward. The brine discharge driven hypersaline pollution dispersion is simulated applying MIKE ...