Using GIS and Multicriteria Analysis to Map Flood Risk Areas of the Tongo Bassa River Basin (Douala, Cameroon)
Bibliographic record
Abstract
Sone Essoh, W.; Onguene, R.; Ndongo, B.; Nshagali, G.; Colmet-Daage, A.; Marie, G.; Iroume, J.; Stieglitz, T.; Besck, F.; Efole Ewoukem, T.; Tomedi Eyango, M.; Etame, J., and Braun, J.J., 2023. Using GIS and multicriteria analysis to map flood risk areas of the Tongo Bassa River Basin (Douala, Cameroon). Journal of Coastal Research, 39(3), 531–543. Charlotte (North Carolina), ISSN 0749-0208. The present study aimed to map the areas at risk of flooding in the Tongo Bassa watershed (42 km2) located in the heart of the Cameroonian economic capital (Douala) in the tropical zone of Central Africa, more precisely in the Wouri Estuary, at the bottom of the Gulf of Guinea. Like most of the world's major cities, Douala is subject to floods. The methodological approach was to identify the flooding risk determinants in the area from an extensive literature review and field surveys and then analyze these factors and map areas at risk of flooding using the analytical hierarchy process approach coupled with the GIS environment. The results revealed that four parameters of the natural environment (elevation, drainage density, distance to the river, and land cover) were the factors that mainly influenced the phenomenon of flooding in the region. Three major classes of flood risk were highlighted: low risk, medium risk, and high risk. To validate the effectiveness of the flood risk map obtained, the flood points collected in the field were cross-checked for ground truth after a flood occurred in the watershed. This cross-check between the resulting map and the flood points using GIS tools showed a good representation of the flooded area. This result is quite interesting because the areas where the risk of flooding is high are consistent with those where flooding is most frequent. The presented results constitute a basic decision support tool for the management of flood zones by the public authorities and the decentralized territorial communities of the city of Douala.
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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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".