Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data
Bibliographic record
Abstract
Continuous monitoring of Water Quality Parameters (WQPs) is crucial due to the global degradation of water quality, primarily caused by climate change and population growth. Typically, Machine Learning (ML) models are employed to retrieve WQPs, but they require a large amount of training samples to accurately capture the data relationships. Even with sufficient training data, discrepancies still exist between values of predicted and in-situ WQPs. This study proposes a Fuzzy Similarity Analysis (FSA) technique to enhance ML estimates of WQPs by using the prediction errors in Effective Training Samples (ETS). The method was successfully applied to retrieve Turbidity (Turb) and Specific Conductance (SC) in Lake Houston, USA, using Sentinel-2 remote sensing data. Three ML algorithms, namely Mixture Density Networks, Support Vector Regression, and Partial Least Squares Regression, were tested to evaluate the method's effectiveness. The results showed that FSA significantly improved the accuracy of all ML predictions. This improvement resulted in up to a 9.15% reduction in Mean Absolute Percentage Error (MAPE) and a 12% increase in R2 for Turb, while for SC, the improvements were 5.47% in MAPE and 7% in R2. The adaptability of the proposed method to other WQPs, various satellite data, and different ML models is promising for monitoring water quality in inland waters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".