Predicting the Occurrence of River Ice Breakup Events in Canada using Machine Learning and Hybrid Modelling
Bibliographic record
Abstract
River ice breakup is a vital process to the morphology and hydrology of many rivers in Canada, often governing peak flows of the river. These events can occur through multiple mechanisms, with the potential for volatile or early breakup events that can have severe impacts to the river. Ice jam flooding can be a potentially devastating result of river ice breakup while early breakup of ice cover in a mid-winter breakup can be unpredictable and greatly alter the remaining ice season. These events are growing increasingly common as a result of climate change, and as a result there is a need to develop prediction tools for these events to aid in decision making support. Past investigations into developing such tools, especially from a data-driven modelling perspective, are challenged by the availability and complexity of the data related to these rare and dangerous to measure events. Therefore, the goal of this dissertation was to develop and apply methods to address the historical challenges and shortcomings in predicting these events through the use of data-driven modelling techniques. This includes: i) development of a stacking ensemble modelling framework for the prediction of ice jam presence during the spring breakup season of a river, utilising variable selection and rare-event forecasting techniques in combination with a comprehensive selection of machine-learning algorithms; ii) return period and trend analysis of mid-winter breakups in conjunction with comprehensive input analysis techniques to identify the key drivers of these events’ severity and develop a means of classifying the flood risk based on hydroclimatic traits; iii) the development of a two-level modelling system for the prediction of the occurrence and timing of mid-winter breakups on a national scale utilising rare event forecasting techniques and imbalanced learning; and iv) development of a novel hybrid semantic and machine learning modelling system in which an ontology is used in conjunction with network analysis techniques to select variables for machine learning models, which is used on a national case study of the prediction of spring breakup timing in Canada. The results of each study in application to their respective case studies demonstrate the effectiveness of the proposed techniques, which are shown to be easily adaptable to other regions or locations. These techniques can form the backbone of decision-making support for communities on rivers that are affected by the unpredictable and oftentimes volatile nature of river ice breakup.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".