Fuel-aware forest fire danger rating system RISICO: a comparative study for Italy

Nicolò Perello; Andrea Trucchia; Mirko D’Andrea; Giorgio Meschi; Farzad Ghasemiazma; Silvia Degli Esposti; Paolo Fiorucci; Luca Ferraris; Andrea Gollini; Dario Negro

doi:10.1071/WF25023

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 34(9)

Fuel-aware forest fire danger rating system RISICO: a comparative study for Italy

Nicolò Perello

^A ^B ^* , Andrea Trucchia

^B , Mirko D’Andrea ^B , Giorgio Meschi ^B , Farzad Ghasemiazma ^A ^B , Silvia Degli Esposti ^B , Paolo Fiorucci ^B , Luca Ferraris ^A ^B , Andrea Gollini ^C and Dario Negro ^C

+ Author Affiliations

- Author Affiliations

^A Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via All’Opera Pia 13, 16145, Genova, Italy.

^B CIMA Research Foundation, Via A. Magliotto 2, 17100, Savona, Italy.

^C Dipartimento della Protezione Civile, Presidenza del Consiglio dei Ministri, via Vitorchiano 2, 00189, Rome, Italy.

^* Correspondence to: nicolo.perello@cimafoundation.org

International Journal of Wildland Fire 34, WF25023 https://doi.org/10.1071/WF25023

Submitted: 31 January 2025 Accepted: 31 July 2025 Published: 25 August 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Land use and climate changes are altering wildfire regimes globally, necessitating forest fire danger rating indices that can accurately identify spatio-temporal fire danger patterns for effective wildfire risk management.

Aims

Many existing models primarily rely on weather conditions, often overlooking fuel and providing daily information. This study introduces the latest version of the RISICO model, developed in the early 2000s and used operationally by the Italian Civil Protection system for decades. RISICO explicitly incorporates fuel into its computations, providing hourly outputs.

Methods

The fuel classification used by RISICO is based on a wildfire susceptibility model developed using machine learning techniques, reducing data requirements. The model’s performance was tested against various fire danger indices, considering the past 16 years of Italian wildfires.

Key results

Analysis of the RISICO outputs demonstrated improved performance in accurately identifying periods and locations of increased fire danger, more precisely narrowing the areas considered at risk compared with other models from the literature.

Conclusions

RISICO proves to be an effective tool for operational use in civil protection contexts, thanks to its ability to identify fire danger conditions accurately and with ease of implementation.

Implications

This study highlights the importance of including fuel characteristics within fire danger models to enhance their discrimination ability and improve wildfire risk management.

Keywords: civil protection, early warning system, fire danger, fire danger rating system, fire weather, fuel, fuel mapping, Italy, risk management, wildfire risk management.

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