Artificial Neural Network Assessment of Groundwater Quality for Agricultural Use in Babylon City: An Evaluation of Salinity and Ionic Composition
Bibliographic record
Abstract
In recent years, the decline in the level of Iraq's rivers necessitated groundwater use for irrigation.A data set collected from 111 wells in the Nile region of Babylon City was analyzed in this research to assess whether or not the groundwater is suitable for irrigation.The studied parameters included electrical conductivity, pH, sulfate, chloride, magnesium, calcium, potassium, sodium, nitrate, bicarbonate, and Total Dissolved Solids (TDS).The quantification of water salinity was achieved through statistical analysis of the examined data, as well as the computation of the sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR), and percentage of dissolved sodium (Na%).Statistical packages for the social sciences (SPSS) software were utilized to generate mathematical models that forecast the quality of groundwater suitable for irrigation, employing artificial intelligence in the form of neural networks.The obtained results indicated that a large proportion of samples fall into the undesirable category, and therefore the groundwater in this region is unsuitable for irrigation.In comparison, it was found that the predicted models have high accuracy and coefficients of determination up to 0.984 for TDS, 0.952 for SAR, 0.918 for Na%, and 0.933 for MAR, with a relative error of no more than 0.056.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".