Extraction of Drainage Networks in the Masamba Watershed Using NASADEM
Bibliographic record
Abstract
The drainage network consists of all river channels that flow toward a reference point. It is bounded by a topographically defined distribution of drainage. The network begins with first-order streams that have no tributaries. Within a drainage network, one or more drainage patterns may be present. This study aims to extract drainage networks and identify drainage patterns in the Masamba watershed. The data used is NASADEM, analyzed through software-based spatial analysis methods. The analysis procedure includes sink filling, flow direction, and flow accumulation, as well as the eight-direction (D8) approach and Strahler’s stream ordering method. The results show that the Masamba watershed exhibits three drainage patterns: trellis, parallel, and dendritic. The trellis and parallel patterns occur mainly in the upstream part of the watershed, while the dendritic pattern dominates the middle and downstream areas. Based on these observations, the Masamba watershed can be classified as having a combined drainage pattern. In addition, stream orders were identified up to six levels, with the sixth-order stream representing the main river in the watershed. In 2020, a flash flood disaster struck the Masamba urban area, which is located in the downstream region. This indicates that drainage patterns have an influence on flooding. Information on drainage networks and patterns can serve as a guideline for governments, planners, and communities in addressing flood risks through sustainable watershed management approaches
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".