Version 1
: Received: 22 October 2021 / Approved: 25 October 2021 / Online: 25 October 2021 (15:03:34 CEST)
Version 2
: Received: 1 November 2021 / Approved: 2 November 2021 / Online: 2 November 2021 (12:57:04 CET)
How to cite:
Xia, C.; Hu, Y. Prediction modeling of Household’s Preparedness of Natural Hazards Mitigation. Preprints2021, 2021100360. https://doi.org/10.20944/preprints202110.0360.v1
Xia, C.; Hu, Y. Prediction modeling of Household’s Preparedness of Natural Hazards Mitigation. Preprints 2021, 2021100360. https://doi.org/10.20944/preprints202110.0360.v1
Xia, C.; Hu, Y. Prediction modeling of Household’s Preparedness of Natural Hazards Mitigation. Preprints2021, 2021100360. https://doi.org/10.20944/preprints202110.0360.v1
APA Style
Xia, C., & Hu, Y. (2021). Prediction modeling of Household’s Preparedness of Natural Hazards Mitigation. Preprints. https://doi.org/10.20944/preprints202110.0360.v1
Chicago/Turabian Style
Xia, C. and Yuqing Hu. 2021 "Prediction modeling of Household’s Preparedness of Natural Hazards Mitigation" Preprints. https://doi.org/10.20944/preprints202110.0360.v1
Abstract
Natural disasters are showing an increase in the magnitude, frequency, and geographic distribution. Studies have shown that individuals’ self-sufficiency, which largely depends on household preparedness, is very important for hazard mitigation in at least the first 72 hours following a disaster. However, for factors that influence a household’s disaster preparedness, though there are many studies trying to identify from different aspects, we still lack an integrative analysis on how these factors contribute to a household’s preparation. This paper aims to build a classification model to predict whether a household has prepared for a potential disaster based on their personal characteristics and the environment they located. We collect data from the Federal Emergency Management Agency’s National Household Survey in 2018 and train four classification models - logistic regression, decision trees, support vector machines, and multi-layer perceptron classifier models- to predict the impact of personal characteristics and the environment they located on household prepare for the potential natural disaster. Results show that the multi-layer perceptron classifier model outperforms others with the highest scoring on both recall (0.8531) and f1 measure (0.8531). In addition, feature selection results also show that among other factors, a household’s accessibility to disaster-related information is the most critical factor that impacts household disaster preparation. Though there is still room for further parameter optimization, the model gives a clue that we could support disaster management by gathering publicly accessible data.
Keywords
Household Disaster Preparation; Natural Hazards Mitigation; Prediction Model
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
