Identification of Potential Quality of Groundwater Using Improved Fuzzy C Means Clustering Method
Bibliographic record
Abstract
The groundwater quality assessment gained more attention among the water quality management stations and researchers. The conventional water quality index method and artificial neural network models are used to assess groundwater. But these models are inadequate to handle data with uncertainty. In this work, we propose an improved Fuzzy C Means clustering method to identify the homogeneous clusters with respect to groundwater quality. For this purpose 1020 groundwater samples data with 7 physiochemical parameters of the year 2019 are collected from West Godavari, Andhra Pradesh, India. The effectiveness of the proposed clustering method is evaluated with two standard clustering methods namely K-means and Fuzzy C Means. The initial selection of the number of clusters and cluster centers determines the success of both the conventional K Means and Fuzzy C Means clustering methods. The proposed improved Fuzzy C Means method identifies the optimal number of clusters based on the water index value. The proposed improved Fuzzy C Means clustering method is implemented on the groundwater data set. The performance is computed with the help of the silhouette score and Davies Bouldin Index. The proposed clustering method outperforms with the existing K Means and Fuzzy C Means with silhouette score of 0.857 and Davies Bouldin Index value of 0.502 when the number of clusters are 4.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".