Digitalisation in geosciences for environmental protection

Abstract Data Science (Digitalization and Artificial Intelligence) became more than an important facilitator in various domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly applied in the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilizing of the best experimental and monitoring data as well as model concepts and tools to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DT) able to mirror or predict the performance of its corresponding existing or future physical implementations including workflows. The call for the Topical Collection was initiated from different actors, including research entities, technical support organizations and nuclear waste management organizations of the European projects EURAD (European Joint Programme on Radioactive Waste Management) and PREDIS (Pre-disposal Management of Radioactive Waste). The Topical Collection attracted a large number of manuscripts, more than eighty of which were published. These articles reveal a strong academic focus on using machine learning to map and assess soil and groundwater resources, hydrology and land use, landslides, and climate protection. They also highlight the core theme of nuclear waste management.