Natural Disaster Clustering Using K-Means, DBSCAN, SOM, GMM, and Mean Shift: An Analysis of Fema Disaster Statistics

Natural disasters tend to ruin people’s lives and infrastructure, which requires comprehensive analysis and understanding to inform effective disaster management and response planning. This research addresses the lack of in-depth analysis of federally declared disasters in the United States using a dataset sourced from FEMA. Through the application of unsupervised learning techniques, including K-means clustering, DBSCAN, self-organizing maps (SOM), and the Gaussian mixture model (GMM), similar types of disasters are clustered based on their frequency. The relationship between disaster type and disaster frequency is analyzed to gain insight into patterns and correlations, facilitating targeted mitigation and adaptation strategies. By using the techniques of clustering, we can accurately group similar disaster types, duration time, occurring time and location of disaster. By implementing these approaches, our study aims to improve the understanding of disaster occurrences and inform decision-making processes in disaster mitigation strategies and adaptation strategies.

@article{Tin2024,
  title     = {Natural Disaster Clustering Using K-Means, DBSCAN, SOM, GMM, and Mean Shift: An Analysis of Fema Disaster Statistics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ting Tin Tin and Yap Jia Hao and Yong Chang Yeou and Lim Siew Mooi and Goh Ting Yew and Temitope Olumide Olugbade and Ali Aitizaz},
  doi       = {10.14569/IJACSA.2024.0150968},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150968}
}