Harnessing Hydrochemical Characterisation and <scp>ANN</scp> ‐Driven Water Quality Modelling for Wetland Sustainability in Sudurpaschim Province, Central Himalaya, Nepal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 it