Authors : Mariecris A. Cairlan; Rachel T. Alegado; Rolaida L. Sonza

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/3tt6t4w5

Scribd : https://tinyurl.com/jtbv37kr

DOI : https://doi.org/10.38124/ijisrt/26mar868

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Disaster preparedness of Local Government Units (LGUs) in the Philippines is hindered by the collisions of disorganized systems, delays in reporting processes, and a scarcity of available current (in real-time) raw data. These restrictions often inhibit the ability for LGUs to make timely and accessible decisions and coordinate effectively when an emergency event occurs. This study has created an AI-Driven Operational Assistant for Disaster Preparedness and Response in Quezon, Nueva Ecija. The Assistant provides real-time access to crucial disaster information, performs risk analysis, and generates automated responses to guide LGU staff in the management of disaster operations. A mixed-methods evaluation conducted with IT experts and LGU end-users, using structured assessment tools, revealed that LGUs were able to achieve high and very high system quality and user acceptability ratings. Ultimately, the findings of this research indicate that the AI-driven Operational Assistant enhances the speed of decision-making, improves how easily disparate data sources are integrated, and improves the overall efficiency of disaster response as compared to traditional manual systems.

Keywords : Artificial Intelligence; Disaster Preparedness; Disaster Response; Local Government Units (LGUs); Decision Support System; Real-Time Data Integration; Disaster Risk Reduction and Management (DRRM); ISO/IEC 25010; Digital Transformation; Emergency Management.

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