Tigris River water surface quality monitoring using remote sensing data and GIS techniques
Bibliographic record
Abstract
Remote sensing and GIS technologies help in decision-making processes to reduce pollution and treatment time. In this study, we aim to investigate using remote sensing data in predicting water quality parameters of the Tigris River. Our approach involves the development of mathematical and statistical models that leverage satellite imagery to predict relevant water parameters. Over 2018 and 2019, fourteen different locations along the Tigris River were surveyed. Measurements for eight parameters were collected simultaneously with satellite images at each location. These parameters included temperature (Temp), electrical conductivity, total dissolved solids (TDS), pH, turbidity, chlorophyll A, blue-green algae, and dissolved oxygen. The spectral bands from Landsat 8 images and spectral indices of soil, vegetation, and water were adjusted as a preprocessing step. Spectral bands and indices were then implemented in the least absolute shrinkage and selection operator (LASSO) to predict the eight water parameters. The evaluation of the prediction model showed that the LASSO model has a determination coefficient (R2) of more than 0.8 for pH and Temp, and the minimum R2 of 0.52 was for TDS. It was found that incorporating spectral indices, as additional features in the prediction models, has significantly improved the models' performance, as demonstrated by an average R2 of 0.7 compared to 0.42 when using spectral bands only. The predictive model for each parameter provided cost-effective alternatives to frequent monitoring of Tigris water quality using field data. The predicted parameters were then utilized to calculate the water quality index (WQI) to indicate water quality along the river. The WQI showed that the river had poor water quality during the year except for April and June, which was very poor. This information will be beneficial in enforcing standards and controlling pollution activities in the study region.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".