Latest additions to the knowledge base

Through this study, the authors develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021–2050 in Venice Italy.

This report presents novel approaches rooted in co-created and tested processes to navigate complexity in using social media and crowd-sourcing in disasters primarily for organisations.

Using an innovative approach blending regional and global climate models with Machine Learning and satellite data, new research offers vital insights into future climate scenarios for Venice, essential for effective coastal risk management.

This paper provides quantitative evidence on the geography of regional readiness to tackle climate change using data from France, Germany, Italy, Poland, and Spain.

This paper provides quantitative evidence on the geography of regional readiness to tackle climate change using data from France, Germany, Italy, Poland, and Spain.

This seismic risk profile of Italy, produced by the Global Earthquake Model (GEM) Foundation summarizes key metrics of seismic risk, allowing stakeholders in risk management to get an overview of the risk in the country.

To better understand the implementation of a micro-scale urban heat island risk assessment, researchers conducted a study using Turin as a subject – the first time that such an in-depth methodology is applied in any Italian city.

Throughout the PSF’s twinning programme, eight municipalities from Spain, Italy, Greece, France and Germany were paired together to offer opportunity to exchange on best practices, lessons learnt, and to evaluate what could be applied in their city.