River salinity mapping through machine learning and statistical modeling using Landsat 8 OLI imagery
Bibliographic record
Abstract
• Good statistical performance doesn’t ensure uncorrelated residuals, and vice versa. • GBDT outperformed statistical, kernel, and neural network models in salinity mapping. • Karun River salinity increases from Gotvand to Ahvaz, due to agriculture & geology. This study uses Landsat 8 OLI imagery and 102 in situ salinity data points to investigate salinity mapping in the Karun River, southwestern Iran. A total of 24 features, including salinity indices and Landsat 8 OLI spectral bands, were assessed using the Random Forest Feature Importance Score (RFFIS), Sobol’ sensitivity analysis, and correlation with salinity to identify the most sensitive features for salinity estimation. These included the Red and Green bands, Salinity index 2–6, Normalized Suspended Material Index (NSMI), and Enhanced Green Ratio Index (EGRI). A total of 24 regression models, including statistical, kernel-based, Neural Network (NN)-based, and Decision Tree (DT)-based models, were evaluated using statistical error metrics and global, as well as local, Moran’s I measures of residual spatial autocorrelation. The DT-based models, specifically Gradient Boosted DT (GBDT), outperformed other models, demonstrating low errors, bias, and non-significant residual spatial autocorrelation. Kernel-based models performed better than conventional linear models, while NN models tended to underfit. Residual spatial autocorrelation analysis indicated that models incorporating spatial information reduced residual autocorrelation. Landsat 8 OLI imagery effectively mapped salinity dynamics, revealing increased salinity from Gotvand to Ahvaz city due to agricultural activities and the Gachsaran formation within the reservoir.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".