Data-driven wildfire risk prediction in northern california

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California

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

Predicting Size of Forest Fire Using Hybrid Model

This paper outlines a hybrid approach in data mining to predict the size of forest fire using meteorological and forest weather index (FWI) variables such as Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), temperature, Relative Humidity (RH), wind and rain. The hybrid model is developed with clustering and classification approaches. Fuzzy C-Means (FCM) is used to cluster the historical variables. The clustered data are then used as inputs to Back-Propagation Neural Network classification. The label dataset having value greater than zero in fire area size are clustered using FCM to produce two categorical clusters,i.e.:Light Burn, and Heavy Burn for its label. On the other hand, fire area label with value zero is clustered as No Burn Area. A Back-Propagation Neural Network (BPNN) is trained based on these data to classify the output (burn area) in three categories, No Burn Area, Light Burn and Heavy Burn. The experiment shows promising results depicting classification size of forest fire with the accuracy of confusion matrix around 97, 50 % and Cohens Kappa 0.954. This research also compares the performance of proposed model with other classification method such as SVM, Naive Bayes, DCT Tree, and K-NN that showed BPNN have best performance. Information and Communication Technology Lecture Notes in Computer Science Volume 8407, 2014, pp 316-327 http://link.springer.com/chapter/10.1007%2F978-3-642-55032-4_3