Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain

Martín, Yago; Antón, María Zúñiga; Mimbrero, Marcos Rodrigues

doi:10.1080/19475705.2018.1526219

Cited by 37 publications

(15 citation statements)

References 51 publications

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“…Among them, a considerable number of references (71) used various ML algorithms to map wildfire susceptibility. Nevertheless, only a few papers considered time series longer than a few years, allowing to assess the predictive performance of the model in the near future [26,31,53] or separately in summer and winter seasons [54].…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Low fire ignition probability and frequency are shown in green, while high fire probability and frequency are shown in red; gray indicates no data also found to be significant for fire ignition frequency. In the Mediterranean region, the strong influence of manmade infrastructures renders the landscape prone to fire ignition and spread (Martínez et al 2009;Miranda et al 2012;Martín et al 2019). In landscapes with a predominance of anthropic activity, such as the Mediterranean countries of Southern Europe, such sources generate the majority of fires (Costa et al 2011;Oliveira et al 2014;Pavlek et al 2016).…”

Section: Discussionmentioning

confidence: 99%

“…A large body of literature exists on the estimation of fire ignition probability and frequency at different global, continental, and regional scales (Miranda et al 2012;Ganteaume et al 2013;Guo et al 2016;Costafreda-Aumedes et al 2017;Oliveira et al 2017;Viedma et al 2018). In particular, a variety of studies have estimated the probability of fire ignition in the Mediterranean Basin using logistic regression (González-Olabarria et al 2007;Catry et al 2009;Martínez et al 2009;Vilar del Hoyo et al 2011), geographically weighted logistic regression (Koutsias et al 2010;Oliveira et al 2014;Rodrigues et al 2014), and machine learning techniques (Oliveira et al 2012;Martín et al 2019). Fire ignition frequency has mainly been investigated by applying counting models, such as Poisson regression (Faivre et al 2014;Boubeta et al 2015;Rodrigues et al 2016) and negative binomial regressions (Quintanilha and Ho 2006).…”

Section: Introductionmentioning

confidence: 99%

Modeling fire ignition probability and frequency using Hurdle models: a cross-regional study in Southern Europe

et al. 2020

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Background Wildfires play a key role in shaping Mediterranean landscapes and ecosystems and in impacting species dynamics. Numerous studies have investigated the wildfire occurrences and the influence of their drivers in many countries of the Mediterranean Basin. However, in this regard, no studies have attempted to compare different Mediterranean regions, which may appear similar under many aspects. In response to this gap, climatic, topographic, anthropic, and landscape drivers were analyzed and compared to assess the patterns of fire ignition points in terms of fire occurrence and frequency in Catalonia (Spain), Sardinia, and Apulia (Italy). Therefore, the objectives of the study were to (1) assess fire ignition occurrence in terms of probability and frequency, (2) compare the main drivers affecting fire occurrence, and (3) produce fire probability and frequency maps for each region. Results In pursuit of the above, the probability of fire ignition occurrence and frequency was mapped using Negative Binomial Hurdle models, while the models’ performances were evaluated using several metrics (AUC, prediction accuracy, RMSE, and the Pearson correlation coefficient). The results showed an inverse correlation between distance from infrastructures (i.e., urban roads and areas) and the occurrence of fires in all three study regions. This relationship became more significant when the frequency of fire ignition points was assessed. Moreover, a positive correlation was found between fire occurrence and landscape drivers according to region. The land cover classes more significantly affected were forest, agriculture, and grassland for Catalonia, Sardinia, and Apulia, respectively. Conclusions Compared to the climatic, topographic, and landscape drivers, anthropic activity significantly influences fire ignition and frequency in all three regions. When the distance from urban roads and areas decreases, the probability of fire ignition occurrence and frequency increases. Consequently, it is essential to implement long- to medium-term intervention plans to reduce the proximity between potential ignition points and fuels. In this perspective, the present study provides an applicable decision-making tool to improve wildfire prevention strategies at the European level in an area like the Mediterranean Basin where a profuse number of wildfires take place.

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“…The majority of papers considered thus far used the entire study period (typically four or more years) to map fire susceptibility, thereby neglecting the temporal aspect of fire risk; however, a few authors have considered various temporal factors to map fire susceptibility. Martín et al (2019) included seasonality and holidays as explanatory variables for fire probability in northeastern Spain. Vacchiano et al (2018) predicted fire susceptibility separately for the winter and summer seasons.…”

Section: Fire-susceptibility Mappingmentioning

confidence: 99%

A review of machine learning applications in wildfire science and management

et al. 2020

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Artiﬁcial intelligence has been applied in wildﬁre science and management since the 1990s, with early applications including neural networks and expert systems. Since then the ﬁeld has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildﬁre science and management. Our overall objective is to improve awareness of ML methods among wildﬁre researchers and managers, as well as illustrate the diverse and challenging range of problems in wildﬁre science available to ML data scientists. To that end, we ﬁrst present an overview of popular ML approaches used in wildﬁre science to date, and then review the use of ML in wildﬁre science as broadly categorized into six problem domains, including: 1) fuels characterization, ﬁre detection, and mapping; 2) ﬁre weather and climate change; 3) ﬁre occurrence, susceptibility, and risk; 4) ﬁre behavior prediction; 5) ﬁre eﬀects; and 6) ﬁre management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildﬁres within a data science context. In total, we identfied 300 relevant publications up to the end of 2019, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artiﬁcial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildﬁre sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML methods to learn on their own, expertise in wildﬁre science is necessary to ensure realistic modelling of ﬁre processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildﬁre research and management communities play an active role in providing relevant, high quality, and freely available wildﬁre data for use by practitioners of ML methods.

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Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain

Cited by 37 publications

References 51 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

Modeling fire ignition probability and frequency using Hurdle models: a cross-regional study in Southern Europe

A review of machine learning applications in wildfire science and management

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