Continuous monitoring of natural physical processes is crucial for understanding their behaviour. The variety of instruments available enhances data collection, aiding in the comprehension of these processes. Long-term data collection reveals trends and patterns, such as seasonal variations, multi-year cycles, and anthropogenic impacts (e.g., deforestation, urbanization, pollution). Conversely, short-term monitoring is vital for real-time decision-making, improving hazard assessment, risk management, and warning systems. Effective data analysis and innovative instrumentation contribute to developing mitigation and adaptation strategies. This session highlights the application of geosciences and geophysical instrumentation, including sensors in natural and laboratory environments, for monitoring natural phenomena and utilizing data systems to study these processes.
The session disseminates advanced research on natural physical processes and the use of scientific principles to address future challenges, including extreme climatic conditions. It encourages novel, interdisciplinary approaches to monitoring, aiming to establish historical baselines. This session seeks to bridge scientific knowledge and technological advancements to improve monitoring and understanding of natural physical processes. The session is inter- and transdisciplinary (ITS), covering topics such as:
1. Destructive and Non-Destructive Sensing Techniques, including contactless and remote sensing methodologies.
2. Monitoring System Developments for understanding hydro-meteorological processes, glaciers, soil erosion, settlements, liquefaction, landslides, earthquakes, volcanic events, and wildfires.
3. Real-Time Monitoring Systems, integrating geoscience data with Building Information Modelling (BIM), digital twins, robotic monitoring, and automation for improved decision-making.
4. Advances in Data Systems for efficient real-time monitoring and processing of large data volumes using Cloud Data Platforms, Distributed and Scalable Data Systems, Real-Time Data Processing, AI, Machine Learning, Data Privacy and Security, and Edge Computing.
5. Storage Technologies and Data Integration, including advancements in Graph Databases, Data Interoperability, and Multi-Model Databases.
6. Intelligent data analysis approaches to analyse accurate and precise interpretation of big data sets driven by various technologies.