使用机器学习进行灾难分析

P. Purushotham, D. D. Priya, A. Kiran
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摘要

灾害分析包括海啸和地震等自然灾害和人为灾害的数据。本文综述了用于流行病和灾害管理的机器学习技术。大多数国家担心罕见的灾害和流行病。灾害和流行病管理使用了物联网、物体传感、无人机、5G和蜂窝网络、基于智能手机的系统和基于卫星的系统。机器学习(ML)方法可以处理灾难和流行病管理中发现的多维、大量数据,非常适合识别和分类等相关任务。机器学习算法可以预测灾害,并帮助完成灾害管理任务,包括建立人群疏散路线和分析社交媒体帖子。机器学习算法还有助于预测流行病、监测流行病传播和诊断疾病。
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Disaster Analysis Using Machine Learning
Disaster analysis includes data on natural and man-made disasters like tsunamis and earthquakes. This article reviews machine learning techniques for pandemic and disaster management. Most nations worry about rare disasters and pandemics. Disaster and pandemic management has used IoT, object sensing, UAV, 5G and cellular networks, smartphone-based systems, and satellite-based systems. Machine learning (ML) methods can handle multidimensional, enormous volumes of data found in disaster and pandemic management and are well-suited for related tasks such as recognition and classification. Machine learning algorithms can predict disasters and help with disaster management duties including establishing crowd evacuation routes and analyzing social media posts. Machine learning algorithms also help anticipate pandemics, monitor pandemic spread, and diagnose diseases.
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