DeSR: Dense Simple Recurrent based Water Quality Prediction with Optimal Feature Selection
Bibliographic record
Abstract
Water is significant for drinking, agriculture, industry, and sustaining ecosystems. Still, maintaining the quality of water is a growing challenge due to pollution, climate change, urbanization, and other human activities. Hence, there is a need for water quality prediction to forecast variation in water quality over time. Hence, a novel water quality prediction technique is introduced in this research using deep learning. Initially, the data for performing the water quality prediction is acquired from the dataset and is pre-processed using the missing data imputation and normalization technique. Then, the optimal best features are extracted using Iterative Lotus Optimization (ILOA) Algorithm. Finally, using the extracted features, the prediction is made by the Dense Simple Recurrent (DeSR) model. The proposed model utilizes the DenseNet-121 and simple recurrent unit for capturing the spatial and temporal features for enhancing the water quality prediction accuracy. The proposed ILOA+DeSR model is evaluated based on R2, MAE, RMSE, and MSE and obtained the outcome of 0.9850, 0.7433, 0.7485, and 0.5603 respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".