Projects

HaMMon: Integrated AI for Enhanced Seasonal Forecasting and Environmental Risk Management

Optimizing environmental risk management with Artificial Intelligence

Funding: ICSC Innovation Funds

Enabling technology: Advanced Modeling and Simulation, Artificial Intelligence, Geographic Information Systems (GIS), High Performance Computing, Image recognition

Sustainable Development Goals

This project expands on the HaMMon initiative by improving its seasonal forecasting system and hazard assessment for extreme events. It will refine the built-environment classification algorithms originally developed under HaMMon’s WP3 and WP4. The project aims to improve the efficiency of environmental data processing and analysis, with the goal of achieving more accurate forecasts of extreme weather events and a clearer understanding of the built environment’s vulnerability to climate-induced hazards.

National Centre for HPC, Big Data and Quantum Computing (ICSC), a project funded by the European Union – NextGenerationEU – and by Italy’s National Recovery and Resilience Plan (PNRR) – Mission 4, Component 2.

Objectives

Improving the mapping of natural extreme event risk for Italy.
Strengthening monitoring activities in Italy, aimed at quantifying the impacts of extreme events.
Improving knowledge of assets exposed to catastrophic risks.
Developing high-resolution medium-range forecasts of extreme events in Italy.
Developing a technological infrastructure for data processing and distribution.

Initial challenge

The study and monitoring of natural hazards and their consequences have increased dramatically in recent years. In the context of a changing climate, these aspects play a crucial role in assessing the local and regional impact of floods, droughts, storm surges, storms, and other extreme phenomena. More broadly, observed climate change has been shown to increase the activity of existing slow-moving landslides and reactivate dormant ones. In addition to climate-related phenomena, earthquakes represent an enormous threat to our country, and in light of the losses suffered in Italy over recent decades, there is a need to improve the seismic risk mitigation strategy.

Solution

The HaMMon project aims to address the problem of natural hazards in Italy, exacerbated by climate change, through an innovative approach that integrates various technologies and methodologies. At the heart of the project is the creation of an advanced IT platform for data management and development activities, focused on the availability of high-performance data and services. In this context, risk monitoring and post-event analysis play a crucial role, especially in assessing damage caused by natural disasters.

An important component of the project is the use of advanced mapping technologies, such as photogrammetry techniques, to create high-resolution 3D models of affected areas, useful for damage assessment. The use of artificial intelligence and the development of innovative algorithms for information extraction and for building classification and identification will also be explored.

The project also makes use of climate models to forecast extreme events and assess their impact, including the generation of AI weather models for modeling extreme weather events.

Finally, the project includes the analysis of the vulnerability of structures and infrastructure, particularly those affected by slow-moving landslides, and the development of fragility curves related to seismic and flood risk. In essence, HaMMon aims to develop a complex, integrated system for the assessment and management of natural hazards in Italy, leveraging the most advanced technologies in monitoring, data analysis, and artificial intelligence.

Benefits

Web application for remote inspection of areas damaged by natural disasters.
Web service for exposing 3D models to third-party applications, with tools for automatic (or semi-automatic) extraction of information on buildings and disasters.
Operational risk management model based on seasonal forecasts.
Weather generation software for risk management applications.
Software tools (library, SDK) for extracting information on buildings based on satellite, aerial, or street-view imagery.
Fragility and loss curves for structural, flood, and seismic risk for specific building taxonomies.
Enhancement and automation of building activity checks.

IFAB’s role

IFAB took on responsibility for the methodological and technological development needed to turn complex environmental data into quantitative risk assessments, usable both for prevention and post-event response. On the methodological side, IFAB’s work addressed three distinct problems:

Identifying and characterizing the risk factors associated with extreme events.
Defining tools for forecasting them over medium-term horizons.
Developing approaches to estimate the impacts generated by catastrophic events on exposed assets.

Alongside this, scientific visualization and artificial intelligence techniques were used extensively to extract meaningful information on the most vulnerable elements of the territory. The entire analytical framework relies on a dedicated technological infrastructure, designed and built by IFAB, for processing and distributing data in support of risk assessment processes. Environmental risk is a domain in which the quality of predictive tools can have a direct impact on people’s lives and on the resilience of the territory.