Community and Infrastructure Resilience Prediction and Management in a Changing Climate
Bibliographic record
Abstract
Climate change poses the most pressing global challenge in recent history. The risks associated with climate change do not only pertain to the rise in temperature, but the accompanying changes in the meteorological and hydrological properties of the planet. Climate change’s impact on the established communities is vast and visible, affecting the functionality of societies on a multitude of ways, and increasingly causing cascading failures and systemic risks (i.e., failures resulting from the interdependent nature of our society systems). This is aggravated by the expansive development of urban areas into exposed, hazard-prone regions. One of the costliest and most frequent hazards resulting from climate change is flood hazard, with its increasing severity and frequency due to the coupling of the aforementioned reasons. This thesis aims at enhancing the resilience of the exposed communities to climate change-induced hazards, with a focus on flood risk, to develop pertinent realistic, proactive, resilience-informed risk management plans. The thesis applies machine learning and data analytics to understand, quantify, and eventually predict climate change-induced flood risk. Descriptive analytics techniques were employed to understand the extent of flood risk on urban communities, resulting in a categorization of the different community responses to flood risk. This categorization is subsequently employed in developing predictive analysis, where global climate models are utilized to predict the changes of the resilience of the exposed communities until the year 2050. Said studies, while revolutionary in nature, serve as a steppingstone in developing a comprehensive, proactive, global disaster management plan. Finally, the thesis narrows its scope by focusing on operationalizing the developed climate resilience methodology considering a single critical infrastructure network and enhances the climate resilience of its risk management plan, set, and operated by its asset owners and decision makers. The approaches developed herein were applied on different datasets for vulnerability identification, loss and resilience prediction, and policy improvement resulting in an overall climate resilience-informed enhancement of the current risk management practices.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".