Flow and Sediment Prediction at Ungauged Basins Using Artificial Intelligence Models and Entropy Index
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The prediction of streamflow and sediment load statistics at locations within ungauged remote basins remains one of the most uncertain modelling tasks in hydrology. The intent of this research was to gain a better understanding of flow and sediment load statistics at ungauged basins through 1) developing artificial neural networks (ANN), and gene expression programming (GEP) models that address the complex nonlinear effect of physio-climatic parameters on flow duration curve (FDC) and sediment rating curve (SRC) statistics, 2) determining the most important physio-climatic parameters impacting FDC parameters (mean, variance), and SRC parameters (rating coefficient and exponent), 3) introducing an entropy parameter, apportionment entropy disorder index (AEDI), that represents precipitation variability, 4) adopting techniques within ANN models to cope with data scarcity including the Dropout method and synthetic minority over-sampling technique (SMOTE), and 5) assessing the impacts of flow regulation on FDC parameters. ANN models trained and tested on 147 stations in Ontario, Canada, revealed that climatic, topographic and land cover characteristics were the most important inputs defining average flow. Topographic and hydrologic characteristics were the most important parameters defining flow variability. ANN and GEP models trained and tested on 260 regulated and unregulated gauging stations across North America showed that drainage area followed by mean annual precipitation, shape factor and AEDI were the most influential parameters on average flow. Regulation was found to affect flow variability and had no significant impact on average flow. Dropout and SMOTE techniques improved model performance. ANN models trained and tested on 94 gauged streams in Ontario, Canada revealed that the rating coefficient is positively correlated to rainfall erosivity factor, soil erodibility factor, and AEDI and negatively correlated to vegetation cover and mean annual snowfall. The rating exponent was found to be positively correlated to mean annual precipitation, AEDI, main channel slope, standard deviation of flow and negatively correlated to the fraction of basin area covered by water. AEDI has been successfully integrated in the FDC and SRC prediction models. Including AEDI parameter in FDC and SRC models improved model performance. This thesis recommends using AEDI in future hydrological modelling research.
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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) | 0.000 | 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 it