Time aware genetic algorithm for forest fire propagation prediction: exploiting multi‐core platforms

Artés, Tomàs; Cencerrado, Andrés; Margalef, Tomás

doi:10.1002/cpe.3837

Cited by 27 publications

(17 citation statements)

References 20 publications

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“…Vakalis et al (2004) used an ANN in combination with a fuzzy logic model to estimate the rate of spread in the mountainous region of Attica in Greece. A number of papers used GAs to optimize input parameters to a physics or empirically based fire simulator to improve fire spread predictions (Abdalhaq et al 2005;Rodriguez et al 2008Rodriguez et al , 2009Artés et al 2014Artés et al , 2016Carrillo et al 2016;Denham et al 2012;Cencerrado et al 2012Cencerrado et al , 2013Cencerrado et al , 2014Artés et al 2017;Denham and Laneri 2018). For example, Cencerrado et al (2014) developed a framework based on GAs to shorten the time needed to run deterministic fire spread simulations.…”

Section: Fire Spread and Growthmentioning

confidence: 99%

“…Such an approach is potentially useful for fire management where it is desirable to predict fire behavior as far in advance as possible so that the information can be enacted upon. This approach may greatly reduce overall simulation time by reducing the input parameter space as also noted by Artés et al (2016) and Denham et al (2012) or through parallelization of simulation runs for stochastic approaches (Artés et al 2017;Denham and Laneri 2018). A different goal was considered by Ascoli et al (2015), who used a GA to optimize fuel models in southern Europe by calibrating the model with respect to rate of spread observations.…”

Section: Fire Spread and Growthmentioning

confidence: 99%

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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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Section: Fire Spread and Growthmentioning

confidence: 99%

Section: Fire Spread and Growthmentioning

confidence: 99%

A review of machine learning applications in wildfire science and management

et al. 2020

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“…With the continuous development of artificial intelligence technology, the use of machine learning algorithms to build forest fire prediction models has drawn increasing attention from the scientific community [18][19][20][21][22][23][24][25][26]. Artificial neural networks are a highly nonlinear dynamic system, which can approximate and simulate any nonlinear function of nonlinear dynamic phenomena such as forest fires with strong fault tolerances [27].…”

Section: Introductionmentioning

confidence: 99%

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Zhong-ke

Chen

et al. 2020

Discrete Dynamics in Nature and Society

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The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

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“…The development of artificial intelligence has led researchers to focus on building a forest fire prediction model using machine-learning algorithms [23][24][25][26][27][28][29][30][31]. Artificial neural networks consist of neurons with adjustable connection weights.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Factor Forest Fire Prediction Model Using Machine Learning Method in China

Li¹,

Feng²,

Zhao³

et al. 2020

Preprint

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Forest fires can cause serious harm in many ways. Studying the scientific prediction of forest fires is an important basis for preventing such fires. At present, there is little research on the prediction of long time series forest fires in China. Choosing a suitable forest fire prediction model is of great importance to China’s forest fire prevention and control work. Based on data on fire hotspots, meteorology, terrain, vegetation, infrastructure, and socio-economics collected from 2003 to 2016, we used a random forest model as a feature-selection method to determine 13 major drivers of forest fires in China (such as temperature, terrain etc.). The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area-under-the-curve value. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and create a spatial distribution map of the areas with high incidences of forest fires. The results show that the prediction accuracy of the four forest fire prediction models is between 75.8% and 89.2%, and the area-under-the-curve value is between 0.840 and 0.960. The random forest model has the highest accuracy (89.2%) and area-under-the-curve value (0.96). It is used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in northeastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region), southeastern China (including Fujian Province and Jiangxi Province) etc. In those areas at high risk of forest fires, the management departments can improve the forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study not only helps in understanding the main drivers of forest fires in China, but it also provides a reference for the selection of high-precision forest fire prediction models and provides a scientific basis for China’s forest fire prevention and control work.

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Time aware genetic algorithm for forest fire propagation prediction: exploiting multi‐core platforms

Cited by 27 publications

References 20 publications

A review of machine learning applications in wildfire science and management

A review of machine learning applications in wildfire science and management

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Research on Multi-Factor Forest Fire Prediction Model Using Machine Learning Method in China

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