Flow Series Generation from Water Depth Data Using Statistical and Machine Learning Models: The Tocache Station Case
Bibliographic record
Abstract
The integration of spline models and Gaussian processes in hydrological studies represents an innovative approach, particularly for developing countries where flow data are scarce but water level records are more accessible.This study focuses on the 'Bridge Tocache' station in Peru, where a procedure was developed and validated to generate historical flow series using these two models.The spline model uses recorded water levels as input, while the Gaussian process model incorporates both water levels and the month of the year, capturing seasonal patterns.To address missing water level data, a stochastic process based on kernel density estimation was applied, segmenting the year into 24 fortnights.The models performed well, achieving coefficients of determination (R² ) above 0.98.Statistical comparisons confirmed no significant differences between the generated and observed flow series in terms of mean, standard deviation, skewness, and kurtosis.The adaptability of these models lies in their independence from geographic-specific assumptions, making them generalizable to other hydrometric stations and regions with limited data availability.This methodology offers a cost-effective alternative for improving hydrological monitoring, reducing reliance on expensive equipment.Its broader applicability extends to sustainable water resource management, enhancing planning and decision-making in regions with limited monitoring capabilities.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".