Harnessing Hydrochemical Characterisation and <scp>ANN</scp> ‐Driven Water Quality Modelling for Wetland Sustainability in Sudurpaschim Province, Central Himalaya, Nepal
Bibliographic record
Abstract
ABSTRACT Wetland ecosystems in the Himalayan region face growing threats from climate change, human activities and environmental degradation. This study introduces an integrated approach to assess and predict the water quality index (WQI) for effective wetland management, focusing on the Alital and Bandatal Lakes in Nepal's Sudurpaschim Province. These lakes were selected due to their distinct ecological and geographical characteristics, as well as differing levels of human impact. A total of 40 water samples (20 from each lake) were collected, and 16 physicochemical parameters, including turbidity (Tur.), total dissolved solids (TDS) and major ions were analysed. Hydrochemical properties were characterised using graphical methods, such as the Gibbs and Piper diagrams and the WQI was computed using the arithmetic average method. The hydrochemical facies analysis indicated that carbonate weathering was the dominant process in both wetlands, with Bandatal showing significant anthropogenic influence. The findings revealed that Alital maintained ‘Excellent’ to ‘Good’ water quality, with an average TDS of 64 mg/L and Tur. of 2.14 NTU, reflecting minimal human impact. In contrast, Bandatal exhibited ‘Poor’ to ‘Unsuitable’ WQI classifications, with TDS averaging 115 mg/L and Tur. reaching 63.6 NTU, highlighting substantial human influences. An artificial neural network (ANN) model was developed to predict the WQI, demonstrating outstanding accuracy with an R 2 of 0.99 for both the training and testing phases. These results underscore the potential of the ANN model for proactive wetland management, aligning with sustainable development goals (SDGs) related to clean water and ecosystem restoration and providing globally applicable insights for wetland conservation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".