Spatial prediction and classification of water quality parameters for irrigation use in the euphrates river (IRAQ) using gis and satellite image analyses
Bibliographic record
Abstract
This study was conducted by analyzing data from satellite image and geographical information system (GIS) to classify water quality parameters of Euphrates River in Iraq for irrigation use.The main purpose of this research was to develop water quality classifi cation models for Euphrates River, Iraq, using remote sensing.The water quality parameters used in this study included total dissolved solids (TDS), chlorides (Cl -), electrical conductivity (EC), and sodium adsorption ratio (SAR).The classifi cation models were used for spatial prediction of these four parameters to determine the water's suitability for irrigation use from the satellite image.GIS techniques were used in the beginning to project the coordinates of 16 stations along the River in LANDSAT satellite image for Iraq map.Positive strong correlations between digital numbers of the satellite image at Band 2 with the water quality parameters in December, 2009, helped to build four regression models between these two variables.These models could be used to predict these four water quality parameters (TDS, Cl -, EC, and SAR) at any point along the River in Iraq from the satellite image directly.The next stage depends on satellite image analyses for the sake of building water quality classifi cation models for Euphrates River on the satellite image to classify each of these water quality parameters according to irrigation use.These water quality classifi cation models can be used to manage the agriculture along the basin of the River and to discover the locations of pollution in the River.The general objective of this research is attaining a classifi cation model that supports the identifi cation, characterization and monitoring of water quality parameters that have an infl uence in irrigation.
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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".