ARCA: A Scalable and Reproducible AI-Driven Workflow Platform for Climate Change and Natural Hazard Applications

Earth System Sciences (ESS) are increasingly characterized by large data volumes and high computational demands, which make complex analyses difficult to manage using ad hoc or manual solutions. This challenge is amplified when heterogeneous data sources, such as Internet of Things (IoT) infrastructures including wireless sensor networks, video cameras and drones, must be combined with high-performance computing (HPC) environments for climate modelling and advanced artificial intelligence (AI) algorithms.

The ARCA (Artificial Intelligence Platform to Prevent Climate Change and Natural Hazards) project, funded by the Interreg IPA ADRION Programme, was designed to respond to these challenges by providing a practical, workflow-based platform aimed at supporting climate change and natural hazard applications and, ultimately, reducing their impacts. The main objective of ARCA is to strengthen the cross-border operational capacity of stakeholders across the Adriatic–Ionian region, involving Italy, Croatia, Montenegro, Albania, Serbia and Greece. The platform supports the monitoring of forest ecosystems through AI-based tools, enabling continuous observation of forest areas and the prediction of multiple natural hazards, including droughts, wildfires and windstorms.

ARCA is built on a modular architecture centered on scientific workflows, which orchestrate multiple-type data ingestion, processing, analysis and AI model execution in a consistent and reproducible manner. The platform integrates big data technologies, workflow management systems and AI components, allowing complex processing chains to be automated while ensuring full traceability of data provenance, computational steps and model configurations. This approach supports FAIR principles and promotes the reuse of data and workflows across different applications and computing environments.

A key strength of ARCA lies in its ability to shield users from much of the underlying technical complexity, such as heterogeneous computing resources, access constraints and large data volumes, while still enabling scalable AI-driven analyses. As a result, researchers and practitioners can focus on scientific and operational questions related to climate impacts and hazard prevention rather than on low-level technical orchestration. In this contribution, we present the overall ARCA architecture together with selected use cases, illustrating how workflow-based approaches can effectively support scalable, transparent and reproducible ESS research in a multinational and federated context like the Adriatic–Ionian region.