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

Zeynep Yüksel; Nazmiye Eligüzel; Suleyman Mete

Makale detayları

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

Yazarlar:

Zeynep Yüksel | Nazmiye Eligüzel | Suleyman Mete

Yayıncı:

Gaziantep University

Ana Başlıklar:

Industrial Engineering

Dergi:

Natural Sciences and Engineering Bulletin Vol.1 Issue 2, pp.17-26

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001332149

pISSN:

3023-8293

Anahtar kelimeler:

Özet:

 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 peoples 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.

 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 peoples 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.

 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 peoples 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.

 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 peoples 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.

Makale detayları

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

Yazarlar:

Zeynep Yüksel | Nazmiye Eligüzel | Suleyman Mete

Yayıncı:

Gaziantep University

Ana Başlıklar:

Industrial Engineering

Dergi:

Natural Sciences and Engineering Bulletin Vol.1 Issue 2, pp.17-26

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001332149

pISSN:

3023-8293

Drone

Fuzzy c-means

LDA

Post-disaster

Unmanned aerial vehicle

Sayı

Makale detayları

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

Yazarlar: Zeynep Yüksel | Nazmiye Eligüzel | Suleyman Mete

Yayıncı: Gaziantep University

Ana Başlıklar: Industrial Engineering

Dergi: Natural Sciences and Engineering Bulletin Vol.1 Issue 2, pp.17-26

Belge Tipi: Makale

Yayın Tarihi: 11/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001332149

pISSN: 3023-8293

Drone

Fuzzy c-means

LDA

Post-disaster

Unmanned aerial vehicle

Sayı

Yazarlar:

Zeynep Yüksel | Nazmiye Eligüzel | Suleyman Mete

Yayıncı:

Gaziantep University

Ana Başlıklar:

Industrial Engineering

Dergi:

Natural Sciences and Engineering Bulletin Vol.1 Issue 2, pp.17-26

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001332149

pISSN:

3023-8293