Predictive Model of Rainfall-Runoff: A Case Study of the Sanaga Basin at Bamendjin Watershed in Cameroon
Bibliographic record
Abstract
In order to reduce the energy deficit recorded in Cameroon, management of watersheds where storage dams are situated plays a vital role. The Bamendjin dam situated upstream of the river Sanaga in Cameroon plays a significant role in regulating the flow of the river Sanaga which is used to generate hydroelectric energy for the South Interconnected Network (SIN) of AES SONEL (the main producer and distributor of electricity in Cameroon) at the power plants of Edea and Songloulou downstream of the Sanaga in Cameroon. This paper proposes a model of the watershed that gives an accurate estimation of the quantity of water that will enter the dam given an estimated future rainfall. The model captures the relationships between rainfall and streamflow and to reliably estimate initial watershed states. While future runoff are mainly dependent on initial watershed states and future rainfall, use of the rainfall-runoff models together with estimated future rainfall can produce skillful forecasts of future runoff which is the basis of this prediction system. The result we obtained is a simulated discharge or hydrograph at the outlet (entrance of the dam). To validate it, a comparison of the simulated flowrate and the observed flowrate is carryout using historic data with the Nash Sutcliffe Efficiency Criterion and we obtained an efficiency of 0.833, meaning that the simulation was good.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".