{"title":"基于自然语言处理的空中野火行动定量风险分析","authors":"Sequoia Andrade, Hannah S. Walsh","doi":"10.2514/1.i011243","DOIUrl":null,"url":null,"abstract":"Aerial wildfire operations are high risk and account for a large number of firefighter deaths. The increasing intensity of wildfires is driving a surge in aerial operations, as well as interest to improve system safety and performance. In this work, wildfire aviation mishaps documented using the Aviation Safety Communiqué (SAFECOM) system are analyzed using a previously developed framework for hazard extraction and analysis of trends. Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard (including the frequency, rate, and severity) are calculated. We examine whether these metrics change over time and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on the rate of occurrence and average severity. The analysis of all SAFECOM reports indicated that the jumper operations hazards were classified as high risk; whereas the hydraulic fluid malfunctions, bucket or tank failures, retardant loading and jettison failures, prescribed burn operations, cargo letdown failures, and severe weather were classified as serious risk. However, when applied to a specific operational scenario, risk levels change across hazards.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"86 1","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Natural-Language-Processing-Enabled Quantitative Risk Analysis of Aerial Wildfire Operations\",\"authors\":\"Sequoia Andrade, Hannah S. Walsh\",\"doi\":\"10.2514/1.i011243\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aerial wildfire operations are high risk and account for a large number of firefighter deaths. The increasing intensity of wildfires is driving a surge in aerial operations, as well as interest to improve system safety and performance. In this work, wildfire aviation mishaps documented using the Aviation Safety Communiqué (SAFECOM) system are analyzed using a previously developed framework for hazard extraction and analysis of trends. Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard (including the frequency, rate, and severity) are calculated. We examine whether these metrics change over time and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on the rate of occurrence and average severity. The analysis of all SAFECOM reports indicated that the jumper operations hazards were classified as high risk; whereas the hydraulic fluid malfunctions, bucket or tank failures, retardant loading and jettison failures, prescribed burn operations, cargo letdown failures, and severe weather were classified as serious risk. However, when applied to a specific operational scenario, risk levels change across hazards.\",\"PeriodicalId\":50260,\"journal\":{\"name\":\"Journal of Aerospace Information Systems\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Aerospace Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2514/1.i011243\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Aerospace Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2514/1.i011243","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
Natural-Language-Processing-Enabled Quantitative Risk Analysis of Aerial Wildfire Operations
Aerial wildfire operations are high risk and account for a large number of firefighter deaths. The increasing intensity of wildfires is driving a surge in aerial operations, as well as interest to improve system safety and performance. In this work, wildfire aviation mishaps documented using the Aviation Safety Communiqué (SAFECOM) system are analyzed using a previously developed framework for hazard extraction and analysis of trends. Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard (including the frequency, rate, and severity) are calculated. We examine whether these metrics change over time and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on the rate of occurrence and average severity. The analysis of all SAFECOM reports indicated that the jumper operations hazards were classified as high risk; whereas the hydraulic fluid malfunctions, bucket or tank failures, retardant loading and jettison failures, prescribed burn operations, cargo letdown failures, and severe weather were classified as serious risk. However, when applied to a specific operational scenario, risk levels change across hazards.
期刊介绍:
This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.