Implementing A Seismic Sensor-Driven Disaster Management System for Efficient Tsunami Early Warnings in the Arabian Peninsula
Bibliographic record
Abstract
With over half of recent global calamities traceable to seismic activities, including earthquakes, volcanic eruptions, and underwater landslides, and their resultant tsunamis, the importance of efficient disaster management cannot be overstated.This study proposes a novel disaster management system, designed to safeguard both human and animal life, particularly along the Arabian Peninsula's coastal areas, which are vulnerable to tsunamis.Seismic activities result from the release of accumulated tectonic plate pressure over millions of years, giving rise to two primary seismic wave types-pressure waves, which travel at a rate of five kilometers per second, and slower shear waves, moving at one kilometer per second.When such activities occur underwater, the subsequent shockwaves can generate tsunamis, which pose significant threats to coastal regions.The proposed disaster management system leverages pressure sensors and wireless communication technology.Deployed on the ocean floor, these sensors detect changes in water pressure caused by displacement, differentiating between standard ocean waves and those induced by seismic activities.Data from these pressure sensors, in conjunction with tidal gauges, are collected and analyzed by a ground station.Based on the severity of the situation, alerts are then issued to relevant parties.This system aims to provide rapid communication to residents in potential disaster zones, facilitating swift evacuation in response to detected seismic activities.To assess the system's effectiveness, a simulation model was created using Proteus, with the results thoroughly analyzed and evaluated.The system's design and implementation is expected to significantly reduce response times to seismic events and tsunamis, thereby enhancing disaster management along the Arabian Peninsula's coastline.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".