A Comprehensive Review on Technological Implementations and Innovations in Cyclone & Flood Disaster Management
Bibliographic record
Abstract
The rise of calamities like floods and cyclones as a result of climate change portrays a picture that is challenging to manage. Therefore, the need for advanced technologies in disaster management is apparent. The focus of this paper is to evaluate the application of Technology, including Geographic Information Systems (GIS), IoT based remote sensing, Flood Sensor Technology, and Artificial Intelligence, and how they amalgamate with Disaster Management Cycle: Reduction, Preparedness, Response, and Recovery. These integrated technologies greatly facilitate the prediction, monitoring, and real time analysis of disaster affecting events to formulate competent mitigation strategies and enhanced preparedness. Further optimization for AI powered platforms and machine learning models are used, facilitating better decision making and situational awareness for affected authorities’ sustainable and rapid response to disasters. Post disaster recovery becomes easier and faster with the use of UAVs, drones, 3D mapping, and other Nanotechnology based devices for efficient damage portrayal of a sutured map of the affected area making infrastructure rebuilding easier. Moreover, smart disaster management systems (SDMS) facilitate communication and collaboration to reduce decision making errors formulating an easier approach for disaster remediation. The paper underscores the issue of taking account of all, specifically the most vulnerable ones. The application of these technologies increases the efficacy and accessibility of the system of disaster management and in dealing with consequences associated with natural 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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".