Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers in such situations and ultimately improve disaster prevention, preparedness, response, and recovery. In this survey article, we highlight relevant work in this area by first focusing on the commonalities of emergency management applications and key challenges that ML algorithms need to address. Then, we present a categorization of relevant works across all the emergency management phases and operations, highlighting the main algorithms used. Based on our review, we conclude that ML algorithms can provide the basis for tackling different activities across the emergency management phases with a unified algorithmic framework that can solve a large set of problems. Finally, through the systematic literature review, we provide promising future directions for utilizing ML algorithms more effectively in emergency management applications. More importantly, we identify the need for better generalization of algorithms, improved explainability, and trustworthiness of ML algorithms with respect to the emergency management personnel, as well as more efficient ways of addressing the challenges associated with building appropriate datasets.

中文翻译：

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用于应急管理的机器学习：调查和未来展望


自然灾害和人为灾害等紧急情况及其连锁效应对整个社会构成严重威胁。机器学习 (ML) 算法非常适合处理此类情况下生成的大量时空数据。因此，多年来，它们一直被用于应急管理，以帮助此类情况下的急救人员和决策者，并最终改善灾害预防、准备、响应和恢复。在这篇调查文章中，我们首先关注应急管理应用的共性以及机器学习算法需要解决的关键挑战，从而重点介绍该领域的相关工作。然后，我们对所有应急管理阶段和操作的相关工作进行分类，重点介绍所使用的主要算法。根据我们的审查，我们得出的结论是，机器学习算法可以通过统一的算法框架来解决应急管理阶段的不同活动，从而解决大量问题。最后，通过系统的文献综述，我们为在应急管理应用中更有效地利用机器学习算法提供了有前景的方向。更重要的是，我们确定需要更好地泛化算法，提高机器学习算法对于应急管理人员的可解释性和可信度，以及更有效的方法来解决与构建适当数据集相关的挑战。