Spatio-temporal erosion modelling for gullies control in Kigali city: Case of Kigali Sector
Bibliographic record
Abstract
Due to land scarcity, hilly topography, and the presence of a significant urban poor population in Rwanda, urban expansion driven by population growth in Kigali city has encroached upon vulnerable ecological areas, resulting in gully erosion. Given the intersection between erosion processes, urban dynamics, and specific locations in urban areas, monitoring temporal variations in erosion is essential for prioritizing resources for controlling gully in areas experiencing the highest erosion. This study aims to model the spatio-temporal patterns of soil erosion to support gully control efforts in Kigali, specifically focusing on the Kigali sector. Potential gully sites were identified using a Digital Elevation Model (DEM) and field measurements. The Integrated Valuation of Ecosystem Services and Tradeoffs-Sediment Delivery Ratio (InVEST-SDR) model was applied to quantify erosion intensity over the period from 2000 to 2022. Data analysis used Pearson’s correlation to evaluate the relationship between estimated erosion levels and observed gully measurements. Zonal statistics analysis was conducted to assess the annual variation in soil erosion intensity at the cell level, aiming to identify areas prone to high erosion. The results indicate that both temporal and spatial erosion intensities increased during the 2010 period, rising to 43 and 150 tons/ha/year, up from 45 and 145 tons/ha/year in 2000. However, in 2015, these rates decreased to 38 and 103 tons/ha/year, before rising again in 2022 to 49 and 133 tons/ha/year. The results from field validation reveal a positive correlation (0.6) between gullies and soil erosion that is exacerbated by rapid urbanization. The findings and analytical approach of this study can support policy and decision-makers in developing cost-effective interventions to manage gullies and reduce soil erosion in the region most affected by soil degradation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".