Advanced GIS-based modeling for flood hazards mapping in urban semi-arid regions: insights from Beni Mellal, Morocco
Bibliographic record
Abstract
Floods are among the most destructive natural disasters, threatening people, the economy and cultural heritage. In Beni-Mellal, mountainous topography accentuates this risk by promoting the rapid flow of water to low-lying areas, where it accumulates more easily. This study maps the flood risk using three statistical methods: Information Value (IV), Weighting Factor (WF) and Weight of Evidence (WoE). A detailed database was built, combining an inventory of floods and key environmental variables, such as slope, proximity to rivers, land use and the Topographic Humidity Index (TWI). The database was built on pre-processed and standardized Sentinel-2 and Landsat 8 satellite images, as well as geological and soil maps, ensuring full coverage and high-definition resolution of 12.5 m to ensure optimal spatial accuracy. The results show that 4.4%–13.6% of the region is classified as very high risk, 13.8%–31.1% at high risk, and 24.5%–31.2% at moderate risk, with increased vulnerability in the southern areas, where land slope and occupation play a major role. The evaluation of model performance reveals that WoE has the highest accuracy and Kappa coefficient, demonstrating its robustness for flood classification. However, WF scores the best AUC scores (88.23% in training, 86.77% in test), making it the most effective model for prediction. The IV approach, although effective, is in third place. These results provide key information for policymakers and urban planners to improve flood risk management and develop appropriate planning strategies to limit flood impacts and build urban resilience to extreme weather events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".