RUSLE-Based Erosion Analysis and Its Contribution to Flooding in Kota Belud, Sabah
Bibliographic record
Abstract
Erosion and flooding are closely linked processes that influence each other through sediment transport and hydrological changes.Erosion reduces the capacity of river channels by depositing sediment, while flooding accelerates soil detachment and transport.In Kota Belud, Sabah, this interplay is intensified by deforestation, agricultural activities, and unsustainable land development.This study employs the Revised Universal Soil Loss Equation (RUSLE) within a GIS environment to assess erosionprone areas and their contribution to flooding.A 5 m × 5 m resolution Digital Elevation Model (DEM) was used to derive the LS factor, while rainfall data, soil series, and land cover were used to compute the R, K, and C factors, respectively.The model was validated using flood-prone area data through an Area Under the Curve (AUC) analysis, resulting in an accuracy of 81.22%, indicating good predictive capability.The erosion susceptibility map revealed that 83.6% of the area has very low susceptibility, while 0.5% falls under high to very high categories.These critical zones likely contribute to sedimentation in rivers, exacerbating downstream flooding.The findings provide essential insights for regional flood management, emphasizing the need for targeted soil conservation and sustainable land-use planning in Kota Belud.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".