Developing Runoff Hydrograph using Artificial Neural Networks
Bibliographic record
Abstract
Conceptual models are considered to be the best choice for describing the runoff process in a watershed. However, enormous requirements for topographic, hydrologic and meteorological data and extensive time commitment for calibration of conceptual models (both physically based and lumped) are often prohibitive factors in considering this option. Artificial neural networks (ANN) can be an efficient way of modeling the runoff process in situations where explicit knowledge of the internal hydrologic processes is not required. An (ANN) is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets. Neural networks provide model-free solutions. This paper highlights the use of ANN for predicting the peak flow, timing and shape of runoff hydrograph, based on causal meteorological parameters. Antecedent precipitation index, melt index, winter precipitation, spring precipitation, and timing are the five parameters used to develop runoff hydrograph on the Red River in Manitoba, Canada. A feed forward artificial neural network is trained by using back-percolation algorithm. Peak flow, time of peak, width of hydrograph at 75% and 50% of peak, base flow, and timing of rising and falling limbs of hydrograph are the output parameters obtained from the neural network to develop a runoff hydrograph. The ANN generated results are evaluated using statistical parameters; % error and correlation. The % errors in simulated and observed peak flow and time of peak is 6 and 3.6 % respectively. Correlation between observed and simulated values of peak flow and time of peak is 0.99 and 0.88, respectively, thus showing potential benefits of using ANN for developing runoff hydrograph.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".