Analysis of Salinity Indices Using SVM Based Approach of Ballari Town, India
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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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".