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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) 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 (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) 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, to the end of 2019, we identified 300 relevant publications in which 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 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 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.
Journal Article
The Misfits Club
When Brian, Hannah, and twins Chris and Sam start their summer holidays, they know it's going to be the end of an era. The Misfits Club is disbanding and they still haven't managed to solve any real mysteries. But when they persuade new club member Amelia to go and investigate a spooky old house, they unexpectedly discover some stolen goods.
Book
Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia
Post-stroke dysphagia is common and associated with significant morbidity and mortality, rendering bedside screening of significant clinical importance. Using voice as a biomarker coupled with deep learning has the potential to improve patient access to screening and mitigate the subjectivity associated with detecting voice change, a component of several validated screening protocols.
In this single-center study, we developed a proof-of-concept model for automated dysphagia screening and evaluated the performance of this model on training and testing cohorts. Patients were admitted to a comprehensive stroke center, where primary English speakers could follow commands without significant aphasia and participated on a rolling basis. The primary outcome was classification either as a pass or fail equivalent using a dysphagia screening test as a label. Voice data was recorded from patients who spoke a standardized set of vowels, words, and sentences from the National Institute of Health Stroke Scale. Seventy patients were recruited and 68 were included in the analysis, with 40 in training and 28 in testing cohorts, respectively. Speech from patients was segmented into 1,579 audio clips, from which 6,655 Mel-spectrogram images were computed and used as inputs for deep-learning models (DenseNet and ConvNext, separately and together). Clip-level and participant-level swallowing status predictions were obtained through a voting method.
The models demonstrated clip-level dysphagia screening sensitivity of 71% and specificity of 77% (F1 = 0.73, AUC = 0.80 [95% CI: 0.78-0.82]). At the participant level, the sensitivity and specificity were 89 and 79%, respectively (F1 = 0.81, AUC = 0.91 [95% CI: 0.77-1.05]).
This study is the first to demonstrate the feasibility of applying deep learning to classify vocalizations to detect post-stroke dysphagia. Our findings suggest potential for enhancing dysphagia screening in clinical settings. https://github.com/UofTNeurology/masa-open-source.
Journal Article
Preparing for a Future War
The First World War had brought Britain’s largest employer, the Post Office, into the forefront of the war effort. Not only was it a major force in the nation’s communications during the war, but it was also the single largest employer of women.When the government began planning for a future war effort, the centrality of the Post Office to these plans was a subject of heated and controversial debates. The mobilization of sufficient ‘manpower’ and the protection of its staff were its chief concerns. This article will examine the nature of the Post Office plan for a future war, and explain how and why this created both tension between the government, and a greater recognition for the need for collaboration between all government departments in the national interest.
Journal Article
Extracorporeal Life Support
By Dan M. Meyer and Michael Jessen. Georgetown, TX: Landes Biosciences, 2001; 136 pp; $45.00
Journal Article
Case Report: Three Farmworkers Who Gave Birth to Infants with Birth Defects Closely Grouped in Time and Place Florida and North Carolina, 2004-2005
There is little evidence linking adverse reproductive effects to exposure to specific pesticides during pregnancy.
In February 2005, three infants with congenital anomalies were identified in Collier County, Florida, who were born within 8 weeks of one another and whose mothers worked for the same tomato grower. The mothers worked on the grower's Florida farms in 2004 before transferring to its North Carolina farms. All three worked during the period of organogenesis in fields recently treated with several pesticides. The Florida and North Carolina farms were inspected by regulatory agencies, and in each state a large number of violations were identified and record fines were levied.
Despite the suggestive evidence, a causal link could not be established between pesticide exposures and the birth defects in the three infants. Nonetheless, the prenatal pesticide exposures experienced by the mothers of the three infants is cause for concern. Farmworkers need greater protections against pesticides. These include increased efforts to publicize and comply with both the U.S. Environmental Protections Agency's Worker Protection Standard and pesticide label requirements, enhanced procedures to ensure pesticide applicator competency, and recommendations to growers to adopt work practices to reduce pesticide exposures. RELEVANCE TO PROFESSIONAL PRACTICE: The findings from this report reinforce the need to reduce pesticide exposures among farmworkers. In addition, they support the need for epidemiologic studies to examine the role of pesticide exposure in the etiology of congenital anomalies.
Journal Article