Tidal sea level variations in the Mediterranean basin, although altered and amplified by resonance phenomena in confined sub-basins (e.g., Adriatic Sea), are generally confined within 0.5 meters and exceptionally up to 1.5 meters. Here we explore the possibility of retrieving sea level measurements using data from GNSS antennas on duty for ground motion monitoring and analyze the spectral outcomes of such distinctive measurements. We estimate one year of GNSS data collected on the Mediterranean coasts in order to get reliable sea level data from all publicly available data and compare it with collocated tide gauges. A total of eleven stations were suitable for interferometric analysis (as of 2021), and all were able to supply centimeter-level sea level estimates. The spectra in the tidal frequency windows are remarkably similar to tide gauge data. We find that the O₁ and M₂ diurnal and semidiurnal tides and MK₃, MS₄ shallow sea water tides may be disturbed by aliasing effects.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.