S2S-AI
Climate change is affecting extreme weather – we identify how and when society is at risk
Seasonal to sub-seasonal (S2S) predictability could provide societies with valuable information on weather-related risk, allowing decision-makers to initiate early warning action plans and to optimize resource management. Using Artificial Intelligence (AI), we are developing a rigorous data-driven framework that enables automatic detection of interpretable physical drivers on S2S timescales. Combining expert knowledge, causal inference and a variety of machine learning techniques, our tool has worked for science. Now we aim for societal impact by making predictions for stakeholders (e.g. NGO’s).
Join the S2S-AI open-source community, developing Artificial Intelligence to improve S2S forecasts
Explore other Projects:
Climecs
CLImatic Manipulation of ECosystem Samples Due to climate change, the world as we knowtoday is rapidly changing. Temperatures are rising, and both rain events and periods of drought become more frequent, intense and longer lasting. These changes are affecting ecosystems and the services they provide (e.g. food production, water storage, nutrient cycling) in unknown ways. […]
COCO Company
Conscious Consumption Company What to do with imperfect food products that retailers won’t buy? COCO Comp identifies overproduced, misprinted, and soon-to-expire items from various producers and manufacturers and connects them directly with offices, clubs, and associations who can acquire these goods for a discounted price. We partner with local and smaller producers to enhance social […]
Sonoptic
AI-based, wearable visual aid technology 1 in 30 Europeans suffers from visual impairment, and 75% of them are unemployed. Taking the form of a pair of glasses, Sonoptic is an AI based solution helps the visually impaired at work, at home and in education by providing three key features: Object identification – to help find […]