Development of Remote Sensing Based Models for Surface Water Quality
Bibliographic record
Abstract
The objectives of this paper were to develop, evaluate, and apply the remote sensing based models for Canadian Water Quality Index (CWQI) and turbidity for the Bow River of Alberta. We used 31 scenes of Landsat‐5 TM satellite data to establish the relationship between the planetary reflectance and the monthly ground measured data for the period of 5 years (i.e. 2006–2010). The four spectral bands (i.e. blue, green, red, and near infrared) were used to obtain the most suitable models from 26 different band combinations. The co‐efficients of determination on the basis of red band were 0.91 for the CWQI model and 0.82 for the turbidity model. The best‐fit models were validated with ground measured data and found that: 72% of the data showed 100% matching for the CWQI model and 83% of the data for the turbidity model. The Landsat‐5 TM based CWQI and turbidity models were applied on all the scenes to obtain five CWQI classes (i.e. excellent, good, fair, marginal and poor), and six classes of turbidity (i.e. 0–10 NTU, 10–20 NTU, 20–30 NTU, 30–40 NTU, 40–50 NTU, >50 NTU). On the basis of percentages obtained for CWQI and turbidity classes, the ranks of years in terms of water quality from best to worst were: 2009, 2006, 2008, 2010, and 2007 respectively. The variation of river water quality in different years of interest was associated with the climatic changes. The most deteriorated water quality noted in two natural sub‐regions included mixed grass and dry mixed grass, which could be related to irrigation‐based farming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".