Wildfires are increasingly affecting ecosystems worldwide with significant impacts on risk exposed human communities. This study is intended to improve and optimize decision-making processes in wildfire risk management by implementing a predictive spatially distributed model of wildfire behaviour. The proposed methodology has been applied to simulate some large and well-documented wildfire events in Umbria and Sardinia regions (Italy), under different climatic and environmental conditions. The predictive model for wildfire behaviour has been based on the Rothermel’s quasi-empirical mathematical model, which investigates propagation-driving parameters, i.e. the local geomorphometrical and meteorological parameters along with the pyrological and phenological characteristics of the local plant communities, to estimate the rate of spread of the fire. Remote sensing-derived data have been analysed along with ancillary data to assess propagation-driving parameters and their spatiotemporal variability in the pre-fire environment by applying and adapting empirical relationships well-established in literature. Satellite estimated propagation-driving parameters have been compared with information collected in the field by the State Forestry Corps and recorded by the regional annual reports on wildfire events, revealing a good predictive ability. A two-dimensional Agent-Based Model with a hexagonal grid has been then developed in order to simulate the wildland surface fire behaviour. Given the wildfire ignition point and a temporal sequence of maps of the rate of spread as inputs, the model returns a map of the simulated burnt area at a given time. The wildfire behaviour model has provided accurate predictions, up to 70% in terms of morphological matching between simulated burnt areas and respective documented historical events boundaries. Obtained results suggest the developed wildfire behaviour model could represent a promising tool in prioritizing suppression interventions by providing maps of wildfires' predictive patterns in near-real time.

Agent-based modelling for wildfire behaviour prediction / D. Voltolina, G. Cappellini, M. Zazzeri, S. Sterlacchini, T. Apuani. ((Intervento presentato al 7. convegno Congresso Nazionale AIGA : 22 settembre tenutosi a Lecco nel 2021.

Agent-based modelling for wildfire behaviour prediction

D. Voltolina
Primo
Conceptualization
;
S. Sterlacchini
Penultimo
Supervision
;
T. Apuani
Ultimo
Supervision
2021-09-23

Abstract

Wildfires are increasingly affecting ecosystems worldwide with significant impacts on risk exposed human communities. This study is intended to improve and optimize decision-making processes in wildfire risk management by implementing a predictive spatially distributed model of wildfire behaviour. The proposed methodology has been applied to simulate some large and well-documented wildfire events in Umbria and Sardinia regions (Italy), under different climatic and environmental conditions. The predictive model for wildfire behaviour has been based on the Rothermel’s quasi-empirical mathematical model, which investigates propagation-driving parameters, i.e. the local geomorphometrical and meteorological parameters along with the pyrological and phenological characteristics of the local plant communities, to estimate the rate of spread of the fire. Remote sensing-derived data have been analysed along with ancillary data to assess propagation-driving parameters and their spatiotemporal variability in the pre-fire environment by applying and adapting empirical relationships well-established in literature. Satellite estimated propagation-driving parameters have been compared with information collected in the field by the State Forestry Corps and recorded by the regional annual reports on wildfire events, revealing a good predictive ability. A two-dimensional Agent-Based Model with a hexagonal grid has been then developed in order to simulate the wildland surface fire behaviour. Given the wildfire ignition point and a temporal sequence of maps of the rate of spread as inputs, the model returns a map of the simulated burnt area at a given time. The wildfire behaviour model has provided accurate predictions, up to 70% in terms of morphological matching between simulated burnt areas and respective documented historical events boundaries. Obtained results suggest the developed wildfire behaviour model could represent a promising tool in prioritizing suppression interventions by providing maps of wildfires' predictive patterns in near-real time.
wildfire behaviour prediction; remote sensing; agent-based modelling
Settore GEO/05 - Geologia Applicata
Associazione Italiana Geologia Applicata e Ambientale (AIGA)
https://www.aigaa.org/gga20/public/programma.pdf
Agent-based modelling for wildfire behaviour prediction / D. Voltolina, G. Cappellini, M. Zazzeri, S. Sterlacchini, T. Apuani. ((Intervento presentato al 7. convegno Congresso Nazionale AIGA : 22 settembre tenutosi a Lecco nel 2021.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/868323
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