Development of a time-varying MODIS/ 2D hydrodynamic model relationship between water levels and flooded areas in the Inner Niger Delta, Mali, West Africa
Bibliographic record
Abstract
The Inner Niger Delta (IND), Mali, West Africa, is a vast floodplain with abundant natural resources that supports the livelihood of about two million people. Ecosystem services in the IND are strongly affected by flood dynamics. An accessible yet accurate flood extent estimation method is crucial for the management of natural resources in the IND. The relationships between water levels and inundation extents are examined for both the rising and receding flood periods, using the outputs of a 2D hydrodynamic model. Inundation extents derived from MODIS images were used to validate the result. The relationship between water levels and flooded areas in the IND changes from year to year due to the amplitude of the incoming flood. Equations were developed to capture that dynamic relationship and estimate flood extent in real-time from September to the end of flood using water levels at Mopti. The relationship is dependent on the maximum water level at Mopti and maximum flooded area which was forecasted using streamflow and precipitation in the Upper Niger Basin. Results show that in addition to forecasting the maximum inundation ahead of time, the inundation predicted with the method shows improvement over the existing formulas by Mahé et al. (2011), Zwart et al. (2005), and Mariko (2003).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".