Machine Learning in Disaster Management: Recent Developments in Methods and Applications

TLDR

Recent climate‑related disasters, including the COVID‑19 pandemic, have caused significant socioeconomic losses, and AI, especially ML and DL, is increasingly employed to mitigate these impacts. This paper reviews research studies from 2017 onward that develop ML and DL methods for disaster management. The review covers studies on disaster and hazard prediction, risk and vulnerability assessment, detection, early warning, monitoring, damage assessment, post‑disaster response, and case studies. The authors analyze recent ML/DL applications in disaster management and discuss their implications, offering directions for future research.

Abstract

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.