Level of Compliance of UDM – Emergency Action Team (EAT) on Earthquake Safety Protocols: Basis for the Integration of AI Enhanced Disaster Response System

This study assesses the compliance of the Universidad de Manila-Emergency Action Team (UDM-EAT) with established earthquake safety protocols, including Republic Act No. 10121 (Philippine Disaster Risk Reduction and Management Act of 2010), CHED Memorandum Order No. 9, and Manila Ordinance No. 8323 (Mendoza, 2025; Nielo, 2023). Given the Philippines' vulnerability to seismic events, this research aims to identify strengths and weaknesses in current disaster preparedness practices. Employing a descriptive qualitative design, data were collected through document analysis and semi-structured interviews with EAT and Security Department members (Guardiano, 2023). Findings reveal that while the UDM-EAT generally complies with safety protocols, significant gaps exist in the Security Department’s engagement and training (Cruz & Tolentino, 2023). The study highlights the need for improved integration of artificial intelligence (AI) technologies to enhance disaster response capabilities (Ivić, 2023). An AI-enhanced disaster response system, termed "Seismic Sentinel," is proposed to facilitate real-time monitoring, predictive alerts, and coordinated emergency responses. This research contributes to the improvement of institutional resilience and readiness, ensuring better protection for the academic community during seismic events.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-15
DOI
https://doi.org/10.5281/zenodo.16431846
Primary Topic
Disaster Management and Resilience
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Level of Compliance of UDM – Emergency Action Team (EAT) on Earthquake Safety Protocols: Basis for the Integration of AI Enhanced Disaster Response System

Zenodo (CERN European Organization for Nuclear Research)
Disaster Management and Resilience
article

Level of Compliance of UDM – Emergency Action Team (EAT) on Earthquake Safety Protocols: Basis for the Integration of AI Enhanced Disaster Response System

article en

Abstract

This study assesses the compliance of the Universidad de Manila-Emergency Action Team (UDM-EAT) with established earthquake safety protocols, including Republic Act No. 10121 (Philippine Disaster Risk Reduction and Management Act of 2010), CHED Memorandum Order No. 9, and Manila Ordinance No. 8323 (Mendoza, 2025; Nielo, 2023). Given the Philippines' vulnerability to seismic events, this research aims to identify strengths and weaknesses in current disaster preparedness practices. Employing a descriptive qualitative design, data were collected through document analysis and semi-structured interviews with EAT and Security Department members (Guardiano, 2023). Findings reveal that while the UDM-EAT generally complies with safety protocols, significant gaps exist in the Security Department’s engagement and training (Cruz & Tolentino, 2023). The study highlights the need for improved integration of artificial intelligence (AI) technologies to enhance disaster response capabilities (Ivić, 2023). An AI-enhanced disaster response system, termed "Seismic Sentinel," is proposed to facilitate real-time monitoring, predictive alerts, and coordinated emergency responses. This research contributes to the improvement of institutional resilience and readiness, ensuring better protection for the academic community during seismic events.

Zenodo (CERN European Organization for Nuclear Research)
University of Manila (PH), Universidad de Moa
Climate action
Openalex Percentile: Top 46%
Disaster Management and Resilience
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.