Identification and Regionalization of Streamflow Routing Parameters Using Machine Learning for the HLM Hydrological Model in Iowa
Bibliographic record
Abstract
Abstract We present a novel approach to determine spatially distributed routing parameters for the distributed hydrological Hillslope Link Model (HLM), an ordinary differential equations‐based streamflow forecasting model implemented and tested in Iowa. We being by developing a technique to determine two model parameters that control the channel routing equation in gauged catchments draining less than 1,300 km2. Then, we implement a parameter regionalization methodology using machine learning classification techniques and a bootstrap procedure, in which we trained 400 Random Forests (RFs) using physical and geomorphological features for classification. We made a regional interpolation using an ensemble of selected RF realizations that exhibited the best performance. We used as benchmark of our results a more straightforward interpolation technique based on USGS Hydrological Units Codes. We performed simulations of the HLM over the entire state of Iowa between 2012 and 2018 using the two regionalization methods, comparing them to the operational model used by the Iowa Flood Center, which applies a single set of parameter values to the entire domain. After evaluating the results at 148 USGS stations, the Random‐Forest approach captures the value of observed peak flows more precisely without losing performance in terms of the Kling Gupta Efficiency index. The improvements obtained using our proposed strategy that uses data, hydrological modeling, and a machine learning technique to identify and regionalize routing parameters are modest, indicating that the parameters that control the rainfall‐runoff transformation dominate uncertainty in our flood forecast model.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".