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Disaster management requires rapid, accurate decision-making based on diverse and often unstructured data sources. Machine learning (ML) algorithms, particularly Naïve Bayes and Decision Trees, offer effective computational frameworks for predicting, classifying, and managing disaster-related information. This paper presents an architectural approach for applying these algorithms in disaster management systems. The Naïve Bayes model provides a probabilistic classification mechanism suitable for early warning systems and risk assessment, utilizing historical and real-time data to predict disaster likelihood. In contrast, Decision Trees offer a hierarchical decision-making structure that aids in resource allocation, damage estimation, and response prioritization. The proposed architecture integrates data collection, preprocessing, model training, and decision-support modules to enhance situational awareness and response efficiency. Comparative analysis indicates that combining both algorithms can improve accuracy, interpretability, and computational speed. The study emphasizes the role of ML-driven architectures in enabling proactive disaster management, supporting both predictive analysis and adaptive response strategies.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: K. Sujatha, sujathakota29@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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