Analysis of Salinity Indices Using SVM Based Approach of Ballari Town, India
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Bibliographic record
Abstract
Soil salinization is a leading cause of soil and land degradation, necessitating early detection for efficient soil management.This study presents an integrated approach combining Remote Sensing and Geographic Information Systems (GIS) to identify saltaffected soils, employing the support vector machine (SVM).The research focuses on the town of Ballari in Karnataka, India, an area highly susceptible to soil salinization with severe consequences.To evaluate, monitor, and implement remedial measures, Ballari was selected as the study area.Data inputs for the SVM model were extracted from nine raster layers derived from the 2011 Landsat 9 imagery and DEM SRTM data.These layers include the Digital Elevation Model (DEM), Topographic Roughness Index (TRI), Topographic Position Index (TPI), Aspect, Slope, Normalized Differential Salinity Index (NDSI), Normalized Differential Vegetation Index (NDVI), Normalized Differential Moisture Index (NDMI), and Normalized Differential Built-up Index (NDBI).Topographical parameters, such as slope, aspect, and other metrics derived from DEM, were found to be instrumental in identifying salt-affected soil due to their ability to indicate land surface texture.Spectral indices NDSI and NDVI, computed using Red and NIR bands, along with the SWIR band, were identified as highly effective in delineating salt-affected soils.Following the layer stacking of these nine layers to form a multiband composite image, the data set was divided into a 70:30 ratio for training and testing, respectively.The model demonstrated an overall accuracy of 89.59% and a Kappa coefficient of 0.84, underlining the efficacy of this approach in predicting soil salinity.
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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 it