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Authors = Vasileios Linardos ORCID = 0000-0003-0841-2926

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20 pages, 1496 KiB  
Article
Utilizing LLMs and ML Algorithms in Disaster-Related Social Media Content
by Vasileios Linardos, Maria Drakaki and Panagiotis Tzionas
GeoHazards 2025, 6(3), 33; https://doi.org/10.3390/geohazards6030033 - 2 Jul 2025
Viewed by 612
Abstract
In this research, we explore the use of Large Language Models (LLMs) and clustering techniques to automate the structuring and labeling of disaster-related social media content. With a gathered dataset comprising millions of tweets related to various disasters, our approach aims to transform [...] Read more.
In this research, we explore the use of Large Language Models (LLMs) and clustering techniques to automate the structuring and labeling of disaster-related social media content. With a gathered dataset comprising millions of tweets related to various disasters, our approach aims to transform unstructured and unlabeled data into a structured and labeled format that can be readily used for training machine learning algorithms and enhancing disaster response efforts. We leverage LLMs to preprocess and understand the semantic content of the tweets, applying several semantic properties to the data. Subsequently, we apply clustering techniques to identify emerging themes and patterns that may not be captured by predefined categories, with these patterns surfaced through topic extraction of the clusters. We proceed with manual labeling and evaluation of 10,000 examples to evaluate the LLMs’ ability to understand tweet features. Our methodology is applied to real-world data for disaster events, with results directly applicable to actual crisis situations. Full article
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28 pages, 3530 KiB  
Review
Machine Learning in Disaster Management: Recent Developments in Methods and Applications
by Vasileios Linardos, Maria Drakaki, Panagiotis Tzionas and Yannis L. Karnavas
Mach. Learn. Knowl. Extr. 2022, 4(2), 446-473; https://doi.org/10.3390/make4020020 - 7 May 2022
Cited by 183 | Viewed by 41091
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 [...] Read more.
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. Full article
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