Building Fire Risk Analysis

Meacham, Brian J.; Charters, David A.; Johnson, Peter; Salisbury, Matthew

doi:10.1007/978-1-4939-2565-0_75

Cited by 22 publications

(10 citation statements)

References 42 publications

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“…In the field of wildfire risk assessment, fire risk has been defined as a quantitative or qualitative indicator of the likelihood that an area would burn in a certain period of time [8][9][10][11]. In this context, modeling fire risks represents a modern tool to support forest protection plans and address fuel management strategies in order to reduce fire consequences [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

et al. 2020

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Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

et al. 2020

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“…In the field of wildfires risk assessment, fire risk has been defined as a quantitative or qualitative indicator of the probability of an area to be source of ignition by natural or artificial means in a certain period of time [5][6][7][8]. In this context, modeling fire risk represents a modern tool to support forest protection plans and to address fuel management strategies in order to reduce fires' consequences [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning based Approach for Wildfire Susceptibility Mapping. The Case Study of Liguria Region in Italy

Tonini¹,

D’Andrea²,

Biondi³

et al. 2020

Preprint

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Wildfire susceptibility maps display the wildﬁres occurrence probability, ranked from low to high, under a given environmental context. Current studies in this field often rely on expert knowledge, including or not statistical models allowing to assess the cause-effect correlation. Machine learning (ML) algorithms can perform very well and be more generalizable thanks to their capability of learning from and make predictions on data. Italy is highly affected by wildfires due to the high heterogeneity of the territory and to the predisposing meteorological conditions. The main objective of the present study is to elaborate a wildfire susceptibility map for Liguria region (Italy) by applying Random Forest, an ensemble ML algorithm based on decision trees. Susceptibility was assessed by evaluating the probability for an area to burn in the future considering where wildfires occurred in the past and which are the geo-environmental factors that favor their spread. Different models were compared, including or not the neighboring vegetation and using an increasing number of folds for the spatial-cross validation. Susceptibility maps for the two fire seasons were finally elaborated and validated and results critically discussed highlighting the capacity of the proposed approach to identify the efficiency of fire fighting activities.

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“…Probabilistic Risk Assessment (PRA) is commonly accepted as one tool for PBD, as acknowledged by the fact that the UK guidance document PD 7974-7 is devoted to PRA for fire safety [5], by its inclusion in the SFPE Handbook of Fire Protection Engineering [49], by the many associated research projects in different fields of fire safety engineering (e.g. [3], [25], [30] and [70]), and by its identification as a research priority by the FORUM of Fire Research Directors [13].…”

Section: Introductionmentioning

confidence: 99%

The Need for Hierarchies of Acceptance Criteria for Probabilistic Risk Assessments in Fire Engineering

et al. 2018

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Building Fire Risk Analysis

Cited by 22 publications

References 42 publications

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

A Machine Learning based Approach for Wildfire Susceptibility Mapping. The Case Study of Liguria Region in Italy

The Need for Hierarchies of Acceptance Criteria for Probabilistic Risk Assessments in Fire Engineering

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