Vasilios Ampas

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly.

This study explores a hybrid AI framework for streamflow forecasting that integrates physically based hydrological modeling, bias correction, and deep learning. HEC-HMS simulations generate synthetic discharge, which a machine learning-based bias correction model adjusts for irrigation-induced discrepancies—improving the Nash–Sutcliffe Efficiency (NSE) from 0.55 to 0.84, the Kling–Gupta Efficiency (KGE) from 0.67 to 0.89, and reducing the RMSE from 1.084 to 0.301 m³/s. The corrected discharge is used as input to a Temporal Fusion Transformer (TFT) trained on hourly meteorological data to predict streamflow at 24-, 48-, and 72-h horizons. In a semi-arid, irrigated basin in Northern Greece, the TFT achieves NSEs of 0.84, 0.78, and 0.71 and RMSEs of 0.301, 0.743, and 0.980 m³/s, respectively. Probabilistic forecasts deliver uncertainty bounds with coverage near nominal levels. In addition, the model’s built-in interpretability reveals temporal and meteorological influences—such as precipitation—that enhance predictive performance. This framework demonstrates the synergistic benefits of combining physically based modeling with state-of-the-art deep learning to support robust, multi-horizon forecasts in irrigation-influenced, data-scarce environments.

Cultural and Creative Industries (CCIs) have emerged as powerful drivers of regional development and revitalization in Europe in recent years. This study explores the impact of CCIs on European regions, focusing on their economic, social, and cultural contributions. Drawing on extensive literature review and empirical research, this study examines the role of CCIs in job creation, GDP growth, innovation, social cohesion, cultural identity, urban regeneration, and the revitalization of declining areas. Data were collected using questionnaires distributed to 345 key players in the Cultural and Creative Industry of Greece. The findings reveal that CCIs significantly enhance job opportunities in European regions, with 74.4% of participants acknowledging their role in reducing unemployment and underemployment. Moreover, 71.4% recognized the substantial contribution of CCIs to regional GDP, highlighting that their economic importance is comparable to traditional industries. CCIs are seen as catalysts for social cohesion and cultural identity, as 75.6% of respondents attested to their role in uniting diverse communities. These results emphasize the sensitivity of CCIs to the existing cultural and historical context of these areas. Furthermore, this study identifies outcomes of regional development and revitalization, including improved education facilities (21.7%), preservation of regional cultural heritage (14.6%), and enhanced business relations (26.7%). Regression analysis demonstrated a strong association between CCIs and regional development and revitalization. This study provides comprehensive insights into the contributions of CCIs to regional development and revitalization in Europe. Policymakers, practitioners, and stakeholders can use these findings to harness the full potential of CCIs for sustainable and inclusive regional development.