A computational system for the Heuristic Forecasting of Fire Risk

5 pages, 5 figures.-- Communication presented at the 6th World Multiconference on Systemics, Cybernetics and Informatics and 8th International Conference on Information System Analysis and Synthesis (SCI/ISAS 2002, Orlando, Florida, Jul 14-18, 2002).This article describes a computational system which forecasts the potential risk of forest fires, by processing a set of meteorological variables so as to produce a fire weather risk index. The system also studies a set of area characteristics, which provides us with long-term static information on potential fire risk. This area-specific information constitutes the interpretation context and can be used to refine the results computed from the weather index

Previsão de vulnerabilidade a incêndios florestais utilizando regressão logística e redes neurais artificiais : um estudo de caso no Distrito Federal brasileiro

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2017.Incêndios florestais são um problema global e queimam milhões de hectares de vegetação nativa todos os anos. O Cerrado brasileiro é a savana neotropical mais rica em biodiversidade do mundo, e uma das regiões mais afetadas por incêndios, sendo considerado um ecossistema tolerante ao fogo. Apesar da adaptação do bioma ao fogo, a alta frequência de incêndios trazida pela ocupação humana tem danificado o ecossistema mais rápido do que ele é capaz de se recuperar. O combate a incêndios é custoso e, portanto, medidas de prevenção de incêndios são a melhor maneira de evitar seus danos em longo prazo. Prever a distribuição espacial da ocorrência de incêndios florestais é um passo importante para a realização do manejo do fogo. Para isto, podem ser utilizados modelos que relacionem a ocorrência do fogo às variáveis que o influenciam. Neste estudo, dois modelos distintos de previsão — Regressão Logística (RL) e uma Rede Neural Artificial (RNA) — foram aplicados à região do Distrito Federal brasileiro, que se encontra inserida dentro do bioma Cerrado. Produtos de área queimada baseados em imagens LANDSAT foram utilizados para gerar a variável dependente, e nove outras variáveis espacialmente explícitas e de origem antropogênica ou ambiental foram utilizadas como variáveis independentes. Os modelos foram otimizados em função da melhor medida de Area under Receiver Operating Characteristic (AUROC, ou simplesmente AUC) a partir da seleção de atributos, e posteriormente validados utilizando dados reais de áreas queimadas. Os modelos mostraram performances similares, mas o modelo utilizando a RNA demonstrou melhor AUC (0.7755), e melhor acurácia ao classificar áreas não queimadas (73.39%), porém pior acurácia média (66.55%) e ao classificar áreas queimadas, para as quais o modelo LR apresentou o melhor resultado (65.24%). Adicionalmente, foi comparada a importância de cada variável aos modelos, contribuindo para o conhecimento das causas principais de incêndios na região. As variáveis demonstraram importâncias similares em ambos os modelos utilizados, e as variáveis de maior importância foram a elevação do terreno e o tipo de uso do solo. Os resultados demonstraram bons desempenhos de todos os modelos testados, mas recomenda-se a execução de mais estudos similares mais detalhados em outras áreas de na savana Brasileira, dado que ainda são poucos os estudos deste tipo.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Wildfires are a global problem, burning millions of hectares of natural forests every year. The Brazilian Cerrado is the richest neotropical savanna of the world in regards to biodiversity, and one of the regions most affected by fires, and also considered a firedependent ecosystem. Despite being adapted to the occurrence of fire, the high frequency of wildfires in the region due to human occupation is damaging the ecosystem faster than it can recover. Fighting fires is costly, and therefore the best way to avoid damages in the long-term is through prevention techniques. Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. For that purpose, models that relate the occurrence of fire to certain variables can be used. In this work, we applied two distinct prediction models — Logistic Regression (LR) and an Artificial Neural Network (ANN) — to the region of Brazil’s Federal District, located inside the Brazilian Cerrado, the largest savanna in South America and the world’s richest Neotropical Savanna. We used LANDSAT based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors were used as the explanatory variables. The models were optimized via feature selection for best Area Under Receiver Operating Characteristic Curve (AUROC) and then validated with real burn area data. The models had similar performance, but the ANN model showed a better AUC value (0.7755) and better accuracy when evaluating exclusively nonburned areas (73.39%), while it had worse accuracy overall (66.55%) and when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. Variables had similar contributions to the models, and the main driving aspects of the distribution of burned areas in the region were the land use type and elevation. The results showed good performance for both models tested, but further research regarding wildfire in the Brazilian savanna is recommended, as such studies are still scarce

Wildfire Risk Prediction for a Smart City

Wildfires are uncontrolled fires that may lead to the destruction of biodiversity, soil fertility, and human resources. There is a need for timely detection and prediction of wildfires to minimize their disastrous effects. In this research, we propose a wildfire prediction model that relies on multi-criteria decision making (MCDM) to explicitly evaluates multiple conflicting criteria in decision making and weave the wildfire risks into the city’s resiliency plan. We incorporate fuzzy set theory to handle imprecision and uncertainties. In the process, we create a new data set that includes California cities’ weather, vegetation, topography, and population density records. The model ranks the cities of California based on their risk of wildfires

A review of machine learning applications in wildfire science and management

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) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 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. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models 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 requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Forest fire warning system

Orientadores: João Frederico da Costa Azevedo Meyer, Laécio Carvalho de BarrosTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: O principal objetivo desta tese é apresentar um sistema de alerta antecipado de incêndio florestal. Este sistema é baseado em três subsistemas acoplados: um \textbf{classificador kNN fuzzy}, que a partir das variáveis distâncias para curso d'água e estrada mais próximos, altitude e tipologia florestal, gera o índice de risco de incêndio florestal, um algoritmo \textit{\textbf{Subtractive Clustering}} e um \textbf{sistema fuzzy} que geram o perigo de incêndio florestal. De modo acoplado o risco e perigo de incêndio florestal podem simular a evolução espaço-temporal do índice de alerta de incêndio. A saída destes sistemas é entendida como pertinências de subconjuntos fuzzy. O alerta de incêndio florestal é utilizado para gerar simulações de propagação de incêndio a partir de metodologia de autômatos celulares. Propomos duas metodologias distintas para obtermos o perigo de incêndio florestal: a primeira consiste em utilizar um sistema fuzzy, que realiza métodos de aprendizagem supervisionada e não supervisionada, para fornecer uma série temporal de valores de perigo e que depende apenas de variáveis climáticas (umidade relativa do ar e precipitação); a segunda envolve um sistema dinâmico fuzzy que gera a mesma série temporal a partir de um sistema baseado em regras fuzzy com as mesmas variáveis de entrada do modelo de aprendizado de máquina. O índice de perigo gerado por esses modelos funciona também como regulador da dinâmica temporal do alerta de incêndio. Os modelos matemáticos são aplicados a dados reais geo-referenciados do Estado do Acre-BrasilAbstract: The main purpose of this thesis is to present a fire forest early warning system. This system is based on three coupled subsystems: a fuzzy kNN classifier, which generates the forest fire risk index from the distances to the nearest watercourse and road, altitude and forest typology; a \textbf{Subtractive Clustering} algorithm and a \textbf{fuzzy system} that create the danger of forest fire. In a coupled way the risk and danger of forest fire can simulate the spatio-temporal evolution of the fire warning index. The forest fire warning is used to generate simulations of fire spread using cellular automata methodology. We propose two different methodologies to obtain the forest fire danger: the first is based upon a fuzzy system that performs supervised and unsupervised learning methods providing a time series of danger values and depends only on climatic variables (air relative humidity and rainfall); The second involves a dynamic fuzzy system which generates the same time series from a system based on fuzzy rules with the same input variables as the machine-learning model. The index of danger generated by these models also acts as a regulator of the temporal dynamics of the warning of fire. The mathematical models are applied to real georeferenced data from the State of Acre-BrazilDoutoradoMatematica AplicadaDoutor em Matemática AplicadaCAPE

A Neural Network Approach for Forestal Fire Risk Estimation

Abstract. This paper describes an intelligent system for the prediction of forest fire risk in Galicia, a region in north-west Spain. The system has been designed to calculate a risk fire index for each of the 360 squares of 10x10 kms into which the area map has been divided digitally. In our research, the problem was approached using a feedforward neural network. The information used to train the network was gathered at five meteorological stations on a daily basis from 1985 to 1999, and consisted of basically meteorological data, namely temperature, humidity and rainfall, in conjunction with previous fire records for the areas represented by squares. Network topologies were tested using 125,156 training data and validated over 13,906 test samples, and that achieving the best performance was the 6-9-1 topology. Finally, our results indicate that the system performs satisfactorily, with a sensitivity of 0.857 and a specificity of 0.768