Precision water quality indices forecasting through an optimized hybrid SMW-LSSVM-R model enhanced by SATLDE and uncertainty analysis
Bibliographic record
Abstract
Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices using advanced machine learning methods. The approach combines the least squares support vector machine (LSSVM) with the Sherman–Morrison–Woodbury (SMW) formula and local weighting techniques to improve the model's capacity to identify local trends and nonlinearities. The hybrid model, SMW-LSSVM-R, integrates the advantages of SMW-LSSVM with ridge regression to provide a balanced and resilient predictive framework. The model parameters are improved by a self-adaptive teaching-learning-based differential evolution (SATLDE) method, attaining optimal performance. Additionally, SATLDE is combined with a ridge feature selection model to identify the key input factors and boost accuracy. The model also employs optimized multivariate variational mode decomposition (OMVMD) using SATLDE algorithm to more effectively assess complex data patterns. When the models were tested at two Iranian stations, Farisat and Molasani, the SMW-LSSVM-R model with a testing R value of 0.975 and an RMSE of 0.990, exhibited better performance than the basic and OMVMD-enhanced models. These findings demonstrate the potential of the proposed hybrid model to offer valuable insights into environmental monitoring and management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".