Enhancing long-term water quality forecasting with a hybrid deep-learning approach integrating MODWT, CNN, and GRU
Bibliographic record
Abstract
Effective water quality forecasting is crucial for managing pollution and mitigating its environmental impacts. Machine learning (ML) and deep learning (DL) models often fail in long-term WQ forecasting accuracy due to complex data patterns and inadequate feature extraction. Although several data-driven forecasting approaches have been employed in the literature for WQ forecasting, to our knowledge, the literature lacks a comprehensive comparison of singular and hybrid data-driven algorithms considering various time steps. Thus, the current investigation introduces a new approach of integrating DL models, MODWT-CNN-GRU, which integrates maximal overlap discrete wavelet transform (MODWT), Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs) to handle dynamic temporal patterns. Comparative analysis indicated that the MODWT-CNN-GRU model outperformed singular Support Vector Regression (SVR), CNN, GRU and hybrid CNN-GRU models. It showed particularly strong performance in weekly forecasting, achieving a 31% improvement over SVR, 28% over CNN, 27% over GRU, and 16% over CNN-GRU.
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.
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 | 0.001 | 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 | 0.001 |
| 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".