Prediction of the Particle Size Distribution of Eroded Sediment from Construction Sites Using Artificial Neural Network Software
Bibliographic record
Abstract
The main objective of this study was to develop an artificial neural network (ANN) model to more accurately predict the event-specific particle size distribution (PSD) of eroded sediments in storm water runoff from construction sites. The eroded sediment PSD is a key design parameter for erosion and sediment control best management practices (BMPs). To complete this task, two active construction sites in Ontario were monitored over a period of two years. This data was supplemented with data collected from laboratory scale experiments on 14 different soils and data from watershed scale stream sediment PSD data. Parent and eroded PSDs were quantified by fitting each to a log normal distribution. The developed ANN model was able to much more accurately (compared to existing regression models) predict the PSD of eroded sediment using easily obtainable inputs (parent log normal parameters, USLE K, C, and P factors, rainfall EI30, flow path, and slope). The ANN has the potential to be used by erosion and control specialists to determine the range of particles to target throughout BMP design.
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 | 0.000 | 0.001 |
| 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 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".