Local Government Unit 4: Emergency Response System with Hazard Mapping Using Microsoft Azure AI and XGBoost Risk Analysis
Authors: Josh Angelo Santiago, Jonathan Dionson, Von Derick Dulfo, Hyziel Gunda, Margarita Pacheco
Advisers
Rommel J. Constantino, DIT
Discipline
IT & management
Abstract
The Emergency Response System with Hazard Mapping using Microsoft Azure AI and XGBoost Risk Analysis was developed to improve emergency management in Barangay Commonwealth, Quezon City. Current operations rely on manual reporting, fragmented records, and delayed decision-making, making it difficult to identify high-risk areas and deploy resources efficiently. The system integrates hazard mapping, predictive risk analysis, and centralized incident management to enhance coordination and situational awareness. By using Microsoft Azure AI and the XGBoost algorithm, the system shifts emergency response from reactive actions to proactive planning, enabling officials to anticipate risks and prioritize vulnerable areas. The study applied Agile Scrum methodology, dividing development into short sprints focused on modules such as incident reporting, hazard mapping, risk prediction, resource allocation, and dashboard visualization. Each sprint included planning, development, testing, and stakeholder review. Microsoft Azure AI was used for data processing and deployment, while XGBoost analyzed historical and real-time data to predict risk levels. Continuous testing ensured proper functionality and alignment with barangay procedures. The system centralized incident reporting, hazard visualization, responder coordination, equipment monitoring, and predictive risk prioritization. These functions were described as improving access to emergency data and response prioritization. No risk-model performance metrics, incident-response times, sample of historical events, user results, or comparison with existing procedures were reported. Hazard mapping and predictive analytics may support preparedness and resource prioritization, while Agile Scrum can facilitate iterative development. Claims of enhanced efficiency, preparedness, decision-making, or community safety require model validation and operational outcome data.
Keywords
agile scrum, emergency response system, hazard mapping, microsoft azure ai, xgboost risk analysis, barangay-based disaster management
How to Cite
Use the format below when citing articles from this publication.
APA 7th Edition
Dionson, J., Dulfo, V. D., Gunda, H., Pacheco, M., & Santiago, J. A. (2026). Local Government Unit 4: Emergency Response System with Hazard Mapping Using Microsoft Azure AI and XGBoost Risk Analysis. Ascendens Asia Singapore – Bestlink College of the Philippines Journal of Multidisciplinary Research Abstracts, 8(2), 121-121. Retrieved from https://ascendens.asia/AASgBCPJMRA/8/2/1164
Ascendens Asia Singapore – Bestlink College of the Philippines Journal of Multidisciplinary Research Abstracts (AASgBCPJMRA)
The Ascendens Asia Singapore–Bestlink College of the Philippines Journal of Multidisciplinary Research Abstracts (AASgBCPJMRA) compiles abstracts of research papers presented at Multidisciplinary Research Fests primarily organized by Ascendens Asia Singapore in partnership with Bestlink College of the Philippines.
Volumes
8 volumes
Issues
6 issues
ISSN
2661-4472
Publisher
Ascendens Asia Publishing Pte. Ltd.