Leveraging Latent Dirichlet Allocation and Fuzzy Clustering for Identifying Key UAV Applications in Disaster Response

Abstract

Over the past few decades, there has been a significant increase in the occurrence of natural disasters, such as earthquakes and landslides, presenting a grave risk to the safety of people's lives and their possessions. Drones, also known as unmanned aerial systems (UAVs), are increasingly attracting the attention of organizations engaged in disaster events, especially in the context of post-disaster emergency response. This research aims to assess the use of UAV applications in the post-disaster phase through a descriptive literature analysis. The evaluation is conducted using the Latent Dirichlet Allocation (LDA) topic modelling and clustering approach, namely the fuzzy c-means algorithm. A total of 433 papers are extracted from the Scopus database. The analysis offers valuable insights into three primary domains: imaging-based damage assessment, emergency communication networks, and vehicle routing optimization. These findings emphasize the significance of technology and streamlined systems in effectively handling complex situations, such as disaster response and network management. By integrating UAVs into disaster response strategies, policymakers can significantly enhance the agility and efficiency of their operations, ultimately saving lives and minimizing the impact of natural disasters on communities. This study can assist in achieving these goals by providing valuable insights and guidance.

Keywords

• Drone
• Fuzzy c-means
• LDA
• Post-disaster
• Unmanned aerial vehicle

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