Knowledge-based Approach for Sustainable Disaster Management: Empowering Emergency Response Management Team
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».