Deciphering technological advancements for efficient disaster management and community resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Disaster management is typically conducted to prevent and respond to disasters. Technological innovations certainly contribute to both, but it remains unclear how. With the rapid technological advancements over the past decade, a comprehensive understanding of the latest technological advancements and their integrated applications in detecting and mitigating the destructive impacts of disasters has emerged as a critical concern for more effective disaster management. This systematic literature review focuses on three primary phases: pre-disaster, during the disaster, and post-disaster. It examines advanced monitoring technologies and sophisticated data analyses to facilitate immediate interventions. The comprehensive disaster management framework proposed in this study evaluates various regions based on their historical disaster occurrences and assigns a specific risk index to each region, thereby enabling the assessment of the preparedness of disaster response systems according to each region's potential. Furthermore, Big Data obtained through surveillance systems and the Internet of Things (IoT) communication sensors are processed using artificial intelligence (AI) and machine learning (ML) algorithms, enhancing computational awareness and sensitivity to changes in detection and notification patterns. By accurately delineating the input and output pathways, this framework aims to optimize supply chain management during emergencies, thereby contributing to achieving the United Nations (UN) Sustainable Development Goals (SDGs) by 2030. Through a systematic review of 135 journal articles, this study highlights the latest technological advancements for expedited disaster detection and response and proposes a structured framework to optimize disaster management and enhance societal resilience. • Risk-based zoning using historical data informs crisis planning and service delivery. • Cloud and 5G/6G tech enable data access and real-time crisis communication. • IoT sensors support early detection and proactive disaster intervention. • AI and ML enhance disaster prediction, prevention, and informed decision-making. • Stable supply chains for energy and food strengthen resilience during disasters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it