IMPACT OF TECHNOLOGY IN HUMANITARIAN ASSISTANCE AND DISASTER RELIEF OPERATION TOWARDS ASSOCIATION OF SOUTHEAST ASIAN NATIONS
Bibliographic record
Abstract
This study examines the transformative role of emerging technologies in enhancing Humanitarian Assistance and Disaster Relief (HADR) operations across the ASEAN region. The need for rapid, coordinated and data-driven responses is critical due to ASEAN's vulnerability to natural disasters. This paper evaluates the technological readiness and integration within ASEAN's disaster management ecosystem, using the Sendai Framework for Disaster Risk Reduction (DRR) as a guiding structure. A qualitative research approach was employed, involving expert interviews with stakeholders from eight ASEAN nations and relevant agencies, including NADMA and MMEA. The findings highlight a growing demand for technology attributes such as interoperability, real-time data acquisition, geospatial intelligence, autonomous systems and resilient communication networks. Technologies such as artificial intelligence (AI), drones, Internet of Things (IoT), and blockchain are identified as enablers for enhanced situational awareness and effective resource coordination. Despite their potential, challenges persist in infrastructure limitations, fragmented data systems, cybersecurity vulnerabilities and funding constraints. Therefore, the paper proposes a Hybrid Technology Framework aligned with the four priorities of the Sendai Framework. The model leverages the DIKW Model to integrate big data analytics with operational decision-making in HADR contexts. The proposed framework is proven to improve the efficiency, scalability and resilience of ASEAN’s disaster response mechanisms. The ASEAN HADR operations must embrace IR4.0 technologies to shift from reactive to proactive disaster management. This transition will not only bridge existing capability gaps but also promote regional cooperation, enhance institutional capacity and foster community-based resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".