Application of Remote Sensing and Geographical Information System (GIS) in Flood Vulnerability Mapping: A Scenario of Akure South, Nigeria
Bibliographic record
Abstract
In flooding, dry land capable of residential, agricultural, and other economic activities is submerged by overflowing water. This causes loss of lives, properties, and destruction of infrastructure. This study applies remote sensing and GIS techniques to produce a flood vulnerability map of the Akure South metropolis. For this study, satellite image data (Landsat 8), location map of Akure South metropolis, SRTM DEM, rainfall data, soil data, and GPS coordinates; acquired during the field survey were integrated to map areas vulnerable to flooding. Using Pairwise Comparison, the various weights of factors constituting flood in the area were acquired. A weighted linear combination and analytical hierarchical process were used to produce the flood hazard and flood vulnerability map. ArcGIS Pro 2.7.3 software was used in spatial and attribute data acquisition, processing, and information presentation. The flood vulnerability results indicated that the very high vulnerability zone occupied 13.9% of the study area, while high vulnerability zone occupied 25.5%. Moderate vulnerability zone occupied 36.8% while low vulnerability zone occupied 23.8% of the study area. The study shows that, remote sensing and GIS can be effectively implemented to analyse and provide results on flood vulnerability required for prompt and effective decision-making on floods.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".