Upgrading the Salinity Index Estimation and Mapping Quality of Soil Salinity Using Artificial Neural Networks in the Lower-Cheliff Plain of Algeria in North Africa
Bibliographic record
Abstract
Since decades ago, the Lower Cheliff plain is under the continuous influence of soil salinization induced by mismanagement of the groundwater resources. The main purpose of this study was to estimate and map soil salinity using both Salinity Index (SI) and Artificial Neural Networks (ANN). In doing so, a total of 796 soil samples of Electrical Conductivity (EC, dS.m–1) measured in laboratory combined to spectral parameters data of Landsat-8 OLI, by applying a Salinity Index (SI) and used also to training the ANN model (80% of total data), the rest of the dataset (20%) was retained for validation with both methods. The results of applying an ANN estimator based on the reflectance values of three bands: green (B3), red (B4) and near-infrared (B5) as learning input neurons, proved their interest in the estimation of EC given a high determination coefficient (R2 = 0.80) between the values of simulated truth and ground, compared to the results obtained using only the SI method giving a moderate precision (R2 = 0.42). Regarding the soil salinity mapping, the two methods generated contrasting results, the SI estimates that 68.5% of the total area is affected by salinity (underestimation) meanwhile the ANN gave an estimation of 78.8%. In a conclusion, the estimation and mapping of soil salinity using the SI method has been upgraded significantly when ANN was involved.
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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 | 0.000 | 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 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".