Modelling Approaches for Subsurface Drainage Water Quality Management
Bibliographic record
Abstract
Abstract Contamination of surface waters by agricultural activities is a serious problem. Two different modelling approaches to simulate nutrient and pesticide transport in subsurface drained soils were investigated in this study. First, artificial neural network (ANN) models, a trainable fast back-propagation (FBP) network and a self-organizing radial basis function (RBF) network, were developed for simulation of NO3--N concentration in tile effluent. Second, a hydrologic model, DRAINMOD, was linked with a chemical transport model, GLEAMS, to simulate chemical transport of atrazine through the soil into subsurface drain outflow. The ANN models and linked DRAINMOD-GLEAMS model were calibrated and validated against experimental data collected at the Greenbelt Research Farm of Agriculture Canada during the years 1988, 1989 and from 1991 to 1994. Several statistical parameters were calculated to evaluate model performance. A comparison of results indicated that the RBF neural network model was superior to the FBP model in predicting drain outflow and NO3--N concentration. Results obtained from the linked DRAINMOD-GLEAMS model demonstrate that atrazine simulations were underpredicted in subsurface drain outflows for spring and fall seasons. Both modelling approaches provide a useful tool for management of fertilizer/manure and pesticides transport through soil and crop root zones into surface water.
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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 | 0.020 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".