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Machine Learning in Disaster Management: Recent Developments in Methods and Applications by Vasileios Linardos; Maria Drakaki; Panagiotis Tzionas; Yannis Karnavas is a Computer Science article available to read on EtoBox.

What is Machine Learning in Disaster Management: Recent Developments in Methods and Applications about?

Recent years include the world’s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017, focusing on ML and DL developed methods for disaster management. In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage assessment and post-disaster response as well as cases studies. Furthermore, some recently developed ML and DL applications for disaster management have been analyzed. A discussion of the findings is provided as well as directions for further research.

Who reads Machine Learning in Disaster Management: Recent Developments in Methods and Applications?

Author

Vasileios Linardos; Maria Drakaki; Panagiotis Tzionas; Yannis Karnavas

Publisher

MDPI AG

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)