GIS-Based Assessment of Flash Flood Susceptibility around Thuwal-Rabigh Region, Saudi Arabia
Bibliographic record
Abstract
Abstract Floods have become more frequent and severe across the globe, resulting in considerable loss of human lives, physical infrastructure, and livelihood. It is also applicable to Saudi Arabia, a country recognized for its arid climate, which has witnessed multiple flood events in the recent past; for example, Jeddah, a coastal Saudi city along the Red Sea experienced floods in 2007 and 2009. Flood susceptibility mapping and its spatio-temporal analysis is a vital component of flood mitigation projects as it identifies the most vulnerable regions of the project area based on physical properties. The present study intends to delineate the flood susceptibility zones, in the Thuwal-Rabigh region, located to the west of Saudi Arabia, by using a geographical information system (GIS) based multi-criteria decision analysis (MCDA) method called the analytical hierarchy process (AHP). The AHP technique was applied to compute the relative weights of nine flood governing factors namely digital elevation model (DEM), topographic wetness index (TWI), slope, normalized difference vegetation index (NDVI), land use/ land cover (LULC), rainfall, distance to waterways, distance to roads and drainage density. The final flood susceptibility map of the study area was obtained and reclassified into five zones (i.e., very low risk, low risk, medium risk, high risk, and very high risk) by using the overlay tool in ArcGIS. The results show that 39% of the study area has a very high to high risk of flooding. The model's sensitivity analysis demonstrates that the maps are reliable and Rainfall, TWI, DEM and slope appears to have a higher influence in the flood risk mapping of the study area. It was also found that Thuwal, city lies in an area of a very high flood risk zone (15%) and needs appropriate measures to ensure sustainable urban development in the future. This study helps to avoid further urban expansion in flood-prone areas and will assist decision-makers in implementing sustainable flood risk management plans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".