The workshop was held at the JRC, Ispra on March 12th and 13th. The workshop was designed to address the growing demand for improved forecasting methodologies in disaster risk management and humanitarian aid. As part of the JRC Portfolio 25 on Enhanced Situational Awareness and the INFORM Warning project, this event intended to facilitate a collaborative space that brings together experts from fields such as artificial intelligence, data science, and social sciences, with a focus on leveraging unique datasets and state-of-the-art machine learning tools. The aim was to refine our ability to predict disaster impacts and foresee humanitarian crises more accurately from 1 to 12 months in advance.
We focus on seasonal weather hazard forecasting, characterising humanitarian emergencies through datasets, and employing machine learning for impact modelling. In particular the sessions covered the following issues: weather hazard forecasting on seasonal/sub-seasonal scales, challenges for disaster impact forecasting, the application of AI for impact forecasting, and strategies for improving data for characterizing humanitarian crises.
Our ambition would be to establish a knowledge-sharing framework that encourages collaboration, advances research, and improves the speed of response, resource distribution, and life-saving capacity in disaster-prone areas. The workshop delved into the following areas: overcoming challenges in seasonal hazard and impact; forecasting ; utilizing data for assessing and characterizing humanitarian crisis; artificial Intelligence for impact modelling. Attendees contributed to the workshop by presenting short talks, lasting about 15 minutes each. The goal of these presentations was to help us create a comprehensive plan for better forecasting and preparation methods research by pinpointing existing gaps and suggesting improvements. The final aim was to establish ongoing partnerships beyond the duration of the workshop.