Applying Remote Sensing and Artificial Neural Networks for Water Quality Index Modeling in the Euphrates River
Why this work is in the frame
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Bibliographic record
Abstract
The Water Quality Index (WQI) is an effective water test that assesses water quality, identifies contaminants, and aids in decision-making.However, it is inefficient to analyze water samples in laboratories due to high costs, time-consuming processes, and limited ability to record temporal or geographical oscillations.Recently, the use of modern technologies such as Remote Sensing (RS) data, Geographic Information Systems (GIS), and Artificial Neural Networks (ANN), in combination with survey data, has confirmed an efficient tool to generate the WQI map of the Euphrates River in Ramadi, Iraq.In the present study, the RS data, such as Landsat 8 and Landsat 9 images, and laboratory tests of samples were used to develop a database for WQI based on spectral reflectance using the radial basis neural network model.The result of this model was then manipulated within ArcGIS 10.8 using the spatial analyst model to generate a digital map of WQI.This model was evaluated using seven criteria, which are correlation coefficient (r), mean absolute error, normalized mean absolute error, lowest absolute error, maximum absolute error, and root square equation of the coefficients (RMSE).The correlation value of the WQI was 0.93, which shows remarkable prediction accuracy.Therefore, this calculation method is effective in calculating the WQI and producing precise digital maps of water quality.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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