Knowledge-based Approach for Sustainable Disaster Management: Empowering Emergency Response Management Team
Bibliographic record
Abstract
Over the past two decades, the impact of disasters has been devastating, affecting 4.4 billion people, resulted in 1.3 million causalities and $2 trillion in economic losses. Post-disaster reconstruction and rehabilitation is a complex process with several dimensions. Government, nongovernmental, and international organizations have their own stakes in disaster recovery programs, and links must be established among them as well as with the community. Concerning post-disaster reconstruction scenario, the most significant factor is prompt decision making based on best possible information available. Effective sustainable post-disaster response is crucial and lies at the heart of disaster management agencies in almost every cautious country around the globe. Development is a dynamic process and disasters provide the opportunities to vitalize and/or revitalize this process, especially to generate local economies, and to upgrade livelihood and living condition. The success of the reconstruction phases, i.e., rescue, relief, and rehabilitation, is mainly dependent on the availability of efficient project teams and timely information to make informed decision. By having the knowledge-based system to make well-informed decisions, combined with the efficiency of a project team and strong coordination, project success should increase. This paper presents a theoretical framework of a knowledge-based approach for enhancing prompt and effective sustainable disaster management. The conceptual model consists of two main IT based components of knowledge- based system, i.e., a knowledge-base and a decision support shell for making more informed decisions for effective, timely and sustainable response in post-disaster reconstruction scenarios. The system is expected to assist in improving reconstruction project processes, coordination, and team building process because the most likely areas on which to focus can be identified during the early stage of the post-disaster scenario. Tapping into the past experiences of post-disaster scenarios, the knowledge-based system provides a wealth of pertinent and useful information for decision makers and will eventually enhance collaborative ventures for sustainable disaster management. The system would be helpful for emergency response management teams to take proactive measures by learning from past similar experiences, making informed decisions related to team building and project coordination processes undertaken by disaster management agencies. Professionals need to work in close cooperation with each other to give rise to a better and more efficient system for sustainable disaster management. Hence, the study is valuable for all professionals involved with research and development of sustainable disaster management strategies.
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".