Saline intrusion into coastal aquifers is one of the major challenges in groundwater resource management in coastal areas. In this study, the behavior of salinity intrusion in the open coastal aquifer of Bandar Abbas has been investigated using a laboratory model and numerical simulation. The SEAWAT numerical model was used to simulate the flow and transport of salinity. The results showed that the numerical model has a high ability to predict the location of the salinity tongue and is in good agreement with the laboratory data. The effects of various factors such as shore slope, initial concentration, and time on the spread of salinity were also analyzed.
This paper presents a comprehensive study of wave modelling in the Northern Indian Ocean, focusing on the dynamics of wave generation, propagation, and interaction with coastal features. The northern Indian Ocean exhibits complex wave dynamics influenced by seasonal monsoons and coastal geometry. Traditional numerical models, while powerful, often struggle to capture fine-scale spatiotemporal variability. This study presents a deep learning approach utilizing a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture to simulate wave parameters near the Iranian coastline. Using ERA5 reanalysis data and satellite altimetry, our model demonstrates improved accuracy in predicting significant wave height and direction compared to conventional methods. The results suggest that deep learning can complement operational forecasting systems with greater computational efficiency. Utilizing numerical models and observational data, wave patterns influenced by monsoonal winds, ocean currents, and seasonal variations are analyzed. The findings aim to enhance understanding of wave behavior for improved maritime safety and coastal management. Utilizing numerical models, can analyze wave patterns influenced by monsoon winds and coastal topography.
An increased focus on the dangers posed by climate change has heightened global interest in the research and development of renewable energy technologies. In other words, the oceans present a vast potential for energy, and investments in this area are rising as renewable energy technologies advance. Studies on ocean thermal energy conversion, offshore wind energy, wave energy, tidal energy, and commercial expansion have resulted in valuable technologies. The potential of these resources contributes to mitigating the risks associated with global climate change; however, it is crucial to safeguard the ocean environment during the development of these technologies. Indeed, if projects are welldefined and appropriately located, and if environmental guidelines are adhered to, the renewable energy resources from the ocean can be harnessed without causing significant harm to the marine ecosystem. This paper aims to explore the existing technologies for renewable energy in the ocean and their current status worldwide.
Accurate estimation of fish populations is critical for sustainable fisheries management, biodiversity conservation, and aquatic ecosystem health. Traditional methods such as mark-recapture and net sampling, while foundational, often suffer from limitations in scale, accuracy, and environmental impact. This paper reviews and synthesizes recent advancements in fish population estimation, including hydroacoustic surveys, environmental DNA (eDNA) analysis, remote sensing, and machine learning-based modeling. Comparative analysis of these techniques are presented, their integration into hybrid frameworks are discussed, and a roadmap for future research that emphasizes precision, scalability, and ecological sensitivity are proposed.
This study investigates the wave energy potential along the southern coast of the Caspian Sea, focusing on Bandar Anzali, Iran. Using ERA5 reanalysis data spanning a decade (2010-2020), seasonal wave power density is estimated and the feasibility of deploying wave energy converters are assessed. Furthermore, significant wave height and energy period to estimate theoretical wave power are analyzed. Results indicate moderate but consistent wave activity, with peak energy during autumn and winter. A hypothetical deployment of point absorbers yields an estimated 2.63 GWh/year, sufficient to power 1,000 households. Environmental, technical, and economic considerations are discussed, offering a roadmap for sustainable coastal energy development in Iran. The findings suggest that small-scale wave energy converters could be feasibly arranged in this region, contributing to Iran's renewable energy portfolio and supporting coastal resilience.
