Handling uncertainty in hydrologic analysis and drought risk assessment using Dempster-Shafer theory
Bibliographic record
Abstract
The aim of this thesis is to enhance some of the hydrologic analyses involved in drought risk assessment (DRA) to uncertainty-driven analyses therefore improving the accuracy and informativeness of DRA. In DRA, risk, or the expected loss from drought hazard is estimated by integrating the magnitude of hazard (i.e., drought severity) with vulnerability (i.e., susceptibility to losses from drought). Most hydrologic analyses including DRA are traditionally performed in a deterministic setting, ignoring data quality and uncertainty issues. Uncertainty can affect the accuracy of modeling results and undermine subsequent decision making. In order to handle uncertainty in DRA, this thesis uses the Dempster-Shafer theory (DST) which provides a unified platform for modeling and propagating uncertainty in the forms of variability, conflict and incompleteness. First, DST is used to model and propagate uncertainty arisen from a high degree of conflict between two datasets of a drought hazard indicator, the snow water equivalent. Four DST combination rules are used for conflict-resolution and results unanimously indicate a high possibility of drought. Second, the Standardized Precipitation Index (SPI) is used as a generic measure of hazard and is linked directly with wildfire risk in current and future climate scenarios. Using DST, modifications are introduced into SPI, enabling the integration of uncertainty analysis with SPI processes. The resulting enhanced SPI can model the effects of long-term shifts in climate normals on drought hazard while simultaneously evaluating the significance of these shifts within the range of surrounding uncertainty. Later, vulnerability to wildfire is simulated using enhanced SPI and two additional variables: evaporation and firefighting capacity. The estimated risk indicates that forests in Okanagan Basin are vulnerable to wildfires during periods of 2040-2069 and 2070-2099 unless the firefighting capacity is enhanced with a presumed rate. Through the successful implementation of DST into DRA processes, this research demonstrates the capability of DST in improving hydrologic analyses and enhancing informativeness in the water resources arena in general.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".