Flood damages reduction with evacuation plans: Life Safety Model implementation on an Italian Basin
Bibliographic record
Abstract
Results of tsunami, earthquake, floods and other natural disasters require a deepened preparedness of population tackling with these natural calamities.Specifically, the analysis of flood-events development and resulting effects highlight the inadequacy of people awareness and preparedness in dealing with this kind of risk.Stakeholders and local authorities are facing these challenges taking into account the increase in urbanization, road networks and human activities expansion in flood-prone areas and consequent sprawl of lifelines risk.Recent researches and studies show that the need of specific flood emergency plans implementation is requested by countries to be get a better preparedness managing population evacuation in affected territory.A first phase in the achievement of flood emergency plans is given by defining flood hazard and risk maps in the flood-prone territory, identifying which part of the territory could be mainly involved and the related hydraulic values consequent to flood flows.This paper describes a modelling approach to manage the evacuation process in case of flood emergency: the research has been pursued using the software Life Safety Model (LSM, (BC Hydro, 2006)).The flood event development in the territory is described in terms of time and space expansion occurrences, allowing LSM to get a feasible description of receptors reactions when the flood occurs.LSM produces baseline data to support population and territory management in case of emergency, reducing and, possibly, avoiding loss of lives which is contemplated as the worst potential damage.The project presented herein studies the applicability of LSM to the Coghinas River lowland valley basin: this has been considered as the pilot basin in the Regional Flood Risk Management Plan for Sardinia, Italy.The study collects information about each receptor located in the area: population (residents and tourists), buildings and infrastructure networks.LSM is developed considering the 2D hydraulic model RFSM-EDA (HR Wallingford Ltd, 2013) in order to describe a flood event of 200 years return period in terms of water depth and velocity evaluation.Flood-event features description on the territory allows LSM to model its evolution, to evaluate potential damages and to easily show, by a visual interface, the emergency development.A proper and detailed representation of the urban network criticalities and social environment during the event should be a safeguard from reaching greater damages: the event simulation verifies the effects of alert/evacuation warnings, as well as activating proper safety actions mainly on crucial points at high level of risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".