Artificial Intelligence in Disaster Risk Reduction and Management in the Philippines: Assessing Its Utilization, Effectiveness, and Challenges

Abstract

The Philippines, ranked among the most disaster-prone countries in the world, has increasingly turned to artificial intelligence (AI) to strengthen its disaster risk reduction and management (DRRM) capabilities. This study assessed the utilization, effectiveness, and challenges of AI technologies in Philippine DRRM across the four thematic areas of the National Disaster Risk Reduction and Management Framework: prevention and mitigation, preparedness, response, and recovery. Using a descriptive-evaluative research design, data were collected through a survey questionnaire distributed to DRRM officers, meteorologists, data scientists, and information technology personnel from national agencies and local government units. Findings reveal that AI is most extensively utilized in preparedness, particularly in early warning systems and flood forecasting, while adoption in post-disaster recovery remains limited. Effectiveness was rated highest for AI-enhanced weather prediction and flood risk modeling, with agencies reporting improved forecast accuracy and reduced warning lead times. However, significant challenges persist, including inadequate data infrastructure, insufficient technical expertise, funding constraints, and interoperability issues among government platforms. The study proposes a strategic framework for accelerating AI integration in DRRM, emphasizing data governance, capacity building, and cross-agency collaboration.

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