Drone as a tool for coastal flood monitoring in the Volta Delta, Ghana
Bibliographic record
Abstract
Monitoring coastal erosion and flooding in deltaic environment is a major challenge. The uncertainties associated with land based methods and remote sensing approaches affect the levels of accuracy, reliability and usability of the output maps generated. This study monitored flooding and erosion activities in a flood prone fishing community (Fuvemeh) in the Volta Delta in Ghana using Unmanned Aerial Vehicles (UAVs) or drone technology. The study revealed that coastal flooding and coastal erosion have destroyed sources of livelihood and increased risk to life and property in the Volta Delta communities. It was identified that between 2005 and 2017 the shoreline has moved several meters inland (over 100 m along some transects) in some areas, while in other areas about 24,057 m 2 land has been gained (about 80 m along some transects) that can serve as natural fish landing site. It emerged that over 77 houses have been destroyed which resulted in the displacement of over 300 inhabitants between 2005 and 2017. The study estimated that about 37% of the total land area in Fuvemeh has been lost as a result of erosion. Coastal erosion and flooding are major environmental challenges in the Volta delta. Coastal erosion has destroyed natural fish landing sites, which has affected the local fishing business (the main source of livelihood) and increased poverty. Coastal flooding has displaced inhabitants from their homes and increased migration from the Fuvemeh community.
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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 | 0.001 | 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".