GIS-Based Soil Erosion Modeling for Assessing Land Suitability in the Urban Watershed of Tallo River, South Sulawesi, Indonesia
Bibliographic record
Abstract
Urban watershed is a discrete and complex system where a diverse number of factors govern its quality and health. Soil erosion by water is the most dominant factor that determines a watershed quality, and considered as one of the most significant forms of land degradation that affects sustained productivity of land use. The principal aim of this paper is to utilise spatial-based soil erosion information to assess land suitability at a watershed level. The specific aim is three-fold: (i) to develop techniques based on a GIS in the parameterisation of a soil erosion model, which is designed for use at a large scale assessment; (ii) to assess and map the spatial distribution of average annual rate of soil losses in; (iii) to employ such related concept as soil loss tolerance to determine land suitability at a watershed level. An analytical procedure is used to analyse an urban watershed of Tallo River, in South Sulawesi, Indonesia, with a total area of 43,422 ha. The procedure is executed using RUSLE (Revised Universal Soil Loss Equation), in a GIS environment, utilising available information in the region (including climate, soil, slope, and land use and land conservation practices), and with the assistance of ground surveys. The results indicate that around 56.5% of the area experience annual soil loos of less than 1 ton/ha/year, while erosion rate of more than 25 ton/ha/year covers a total area of 8.9%. Due to a good ground cover in forested land, most of the slopping areas have actual soil losses of 1-5 ton/ha/year. This study reveals that areas categorized as high risk, where only forest cover allowed consist of 9.4%, and those with very low risk cover a total area of 5.4%. Most of the study region (around 84%) experience moderate and low erosion risk, and suitable for cropping with special management practices (CS) + perennial crops (PC) + grass (GR) + and forest (FR). This study suggests that the outputs of this modeling procedure can be used for the identification of land management units based on degradation levels, as well as the most suitable land use to be practiced on individual land units on a sustainable basis.
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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 | 0.000 | 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".