Water quality assessment of Elgo river in Ethiopia using CCME, WQI and IWQI for domestic and agricultural usage
Bibliographic record
Abstract
The increasing demand for water due to the escalation in population and aggressive agricultural activities for drinking and irrigation purposes in the rural areas of Ethiopia has put tremendous stress on water requirements. The Elgo River in southern Ethiopia is deteriorating due to sedimentation, soil erosion, stormwater runoff, and anthropogenic activities. Elgo village faces water shortages and a lack of safe drinking water. The purpose of this research was to identify the extent of pollution in Elgo River water using the Canadian Council of Ministers of the Environment (CCME), Water Quality Index (WQI), and Irrigation Water Quality Index (IWQI). A total of 12 water samples were collected from 3 river sampling sites for the dry and wet seasons to test the physicochemical and biological parameters. Results obtained were: turbidity (46.5-156) NTU, colour (103.65-606.5) TCU, EC (182-268) μS/cm, TDS (192.5-275.5) mg/l, TSS (680-2774) mg/l, Ca2+ (22-45) mg/l, Mg2+ (19.5-23.5) mg/l, Cl- (10.5-16.65) mg/l, and SO42- (17.18-47) mg/l for both the dry and wet seasons, respectively. The CCME WQI revealed that the overall results were 38.38 for the dry season and 36.6 for the wet season for drinking water parameters. The CCME WQI categorization indicates that the Elgo River water is classified as poor, with results ranging from 0 to 44. For irrigation purposes 10, parameters such as SAR, PS, PI, MAR, KI, RSC, EC, SSP, TH, and %Na were examined to compute indices using the IWQI model. The overall result of water quality indicated that IWQIs of 81.4 and 62.14 are good for the dry season and poor for the wet season, respectively. This research provides a thorough analysis through modelling to determine the suitability of water for different purposes for the tribal and backward communities of the area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".