LOCAL GOVERNMENT UNIT 1: EMERGENCY RESPONSE AND DISASTER MANAGEMENT SYSTEM (AI CHAT ASSISTANT, REAL-TIME EMERGENCY ALERTS, COMMUNITY PORTAL, AND GIS-INTEGRATED EMERGENCY SUPPORT OPERATIONS)

Abstract

The frequent natural disasters in the Philippines highlight the urgent need for effective disaster management systems. These events also expose the limitations of traditional emergency response mechanisms, which are often characterized by slow reaction times, poor communication, and outdated manual processes that fail to efficiently allocate the necessary human and physical resources during large-scale emergencies. This study introduces the Local Government Unit: Emergency Response and Disaster Management System, designed to address these problems. The system is technology-driven, integrates real-time communication, and utilizes the situational awareness provided by mapping software to enhance response operations. The effectiveness of the Emergency Response and Disaster Management System is evaluated using a mixed-methods research design that combines qualitative and quantitative approaches. This method allows for the collection of a wide range of data related to user experience and system performance. The study focuses on barangays within local government units that are vulnerable to natural disasters such as floods and typhoons. The participants include local government officials, residents, and emergency response personnel selected as the intended sample. A total of fifty-three residents, LGU officials, and emergency responders assessed the Emergency Response and Disaster Management System during real-life emergencies. With 95% confidence, real-time alerts, incident reporting, and disaster response efficiency received 100% positive feedback. The AI Chat Assistant scored 86.8% for timely responses and 90.5% for reliability. Usability and interface were rated 94.3% positive, GIS mapping accuracy 94.3%, and feasibility in emergencies 98.1%, with 90.6% of respondents recommending adoption by LGUs. These results demonstrate strong user approval and highlight the system’s potential to enhance disaster response operations. The study confirmed the system’s effectiveness, showing a 95% confidence level in key features such as real-time reporting, emergency alerts, GIS mapping, and the AI Chat Assistant. These features received overwhelmingly positive feedback, with 100% of respondents finding the alert system helpful and more than 90% recommending adoption by local government units. However, the findings also indicated that for improved scalability and reliability, enhancements to the system’s AI and connectivity components are necessary.