Identification of robust catchment classification methods for Sahelian watersheds
Bibliographic record
Abstract
The study was conducted in a Sahelian watershed located in Burkina Faso (West Africa). In this study, an inter-comparison procedure is proposed to investigate the effects of implementing various sets of explanatory variables and clustering algorithms on developing hydrologically homogenous regions. Six different sets of explanatory variables considered in this framework are generated using the combinations of topographic, land-use, climatic, and hydrological attributes. Also, seven different linear and nonlinear clustering techniques are implemented using the combinations of Principal Component Analysis (PCA), Non-linear Principal Component Analysis (NLPCA), Self-Organizing Maps (SOM), and K-means algorithm. The mean and maximum annual runoff are considered as two variables of interest for conducting a comparison and identifying the most robust classification methods. The study results indicate that the monthly Bagnouls-Gaussen index (BGI) is the most robust set of explanatory variables to be used for identifying the hydrologically homogenous regions considering both mean and maximum annual runoff. Additionally, compared with BGI, the combination of topographic and land-use attributes can provide competitive results while the land-use attributes alone cannot capture the hydrological heterogeneity of the catchments. Moreover, interestingly, the comparison results show that regardless of its simplicity, the K-means algorithm is superior to the other clustering techniques in terms of generating hydrologically homogenous regions based on the monthly BGI. • Implemented catchment classification intercomparison framework. • Thoroughly examined various sets of explanatory variables and clustering methods. • Consider the effects of the number of clusters in the inter-comparison process. • The Monthly Bagnouls-Gaussen Index excels in classifying Sahelian watershed regions. • K-means outperforms in finding homogeneous regions using Bagnouls-Gaussen Index.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".