Prediction of Chlorophyll and Phosphorus in Lake Ontario by Ensemble of Neural Network Models
Bibliographic record
Abstract
<p>Chlorophyll-a (CHLA) and total phosphorous (TP) are key indicators for water quality and eutrophication in lakes. It would be a great help to water management if CHLA and TP could be predicted with certain leading time to ensure water quality control measures could be implemented. Since eutrophication is the results of a complex bio-chemical-physical processes involving in pH, temperature, dissolved oxygen (DO) and many other water quality parameters, the discover of their internal correlations and relationships may help in the predication of CHLA and TP. In this study, a long term (20 years) water quality data including CHLA, TP, total nitrogen (TN), turbidity (TB), sulphate, pH, and DO collected in Lake Ontario by the Environment and Climate Change Canada agency were obtained. These data were analyzed by using a group of Neural Network (NN) models and ensemble strategies were evaluated in this study. One particular ensemble of the following NN models, namely, back propagation, Kohonen, probabilistic neural network (PNN), generalized regression neural network (GRNN), or group method of data handling (GMDH) were selected which has higher goodness of fit and shows robustness in model validation. Comparing with one single NN model, the ensemble model could provide more accurate predictions of CHLA and TP concentration in Lake Ontario and the predication of CHLA and TP would be helpful in lake management, eco-restoration and public health risk assessment.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".