Suspended Sediment Concentration Modeling Using Conventional and Machine Learning Approaches in the Thames River, London Ontario
Bibliographic record
Abstract
Water resources management, hydraulic designs, environmental conservation, reservoir operation, river navigation and hydro-electric power generation all require reliable information and data about suspended sediment concentration (SSC).To predict such data, direct sampling and sediment rating curves (SRC) are commonly used.Direct sampling can be risky during extreme weather events and SRC may not provide satisfactory or dependable results, so engineers are developing new precise forecasting approaches.Various soft computing techniques have been used to model different hydrological and environmental problems, and have showed promising results.Prediction of SSC is a site-specific phenomenon and ought to be modeled for every river and creek.In this study, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models were compared with conventional SRC and linear regression methods.Using different combinations of observed SSC data and simultaneous stream discharge, water temperature, and electrical conductivity data for the Thames River at Byron Station, London Ontario from 1993 to 2016, several models were trained.Each model was evaluated using mean absolute error, root mean square error and the Nash-Sutcliffe efficiency coefficient.Results show that ANN models are more accurate than other modeling approaches for predicting SSC for this river.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".