Enhancing Flood Prediction Accuracy through Integration of Meteorological Parameters in River Flow Observations: A Case Study Ottawa River
Bibliographic record
Abstract
Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is crucial to incorporate temperature as an input variable in flood prediction models with machine learning algorithms. This inclusion enables a comprehensive understanding of the intricate dynamics involved, particularly the impact of heatwaves on snowmelt, allowing for more accurate flood prediction. This paper presents a novel machine learning approach called the Adaptive Structure of the Group Method of Data Handling (ASGMDH) for predicting daily river flow rates, incorporating measured discharge from the previous day as a historical record summarizing watershed characteristics, along with real-time data on air temperature and precipitation. To propose a comprehensive machine learning model, four different scenarios with various input combinations were examined. The simplest model with three parameters (maximum temperature, precipitation, historical daily river flow discharge) achieves high accuracy, with an R2 value of 0.985 during training and 0.992 during testing, demonstrating its reliability and potential for practical application. The developed ASGMDH model demonstrates high accuracy for the study area, with a significant number of samples having a relative error of less than 15%. The final ASGMDH-based model has only a second-order polynomial (AICc = 19,648.71), while it is seven for the classical GMDH-based model (AICc = 19,701.56). The sensitivity analysis reveals that maximum temperature significantly impacts the prediction of daily river flow discharge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".