Flood forecasting system of urban areas in South Korea
Bibliographic record
Abstract
The needs of safety and the security of the homeland from unexpected natural disasters have been growing among people recently.Flood damages have been recorded every year and those damages are greater than the annual average of 2 billion US dollars since 2000 in South Korea.Due to the rapid growth of urbanization and climate change, the frequency of concentrated heavy rainfall has increased causing urban flood that results in casualties and property damage.Despite the emerging importance of a flooding situation, the studies related to the development of an integrated management system for reducing floods are insufficient in South Korea.In addition, it is difficult to reduce floods effectively without developing integrated operation system taking into account of sewage pipe network configuration with the river level.Since the floods result in increasing damages to infrastructure, as well as life and property, structural and non-structural measures should be urgently established in order to reduce the flood effectively.Therefore, in this study, we developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting in urban areas.The purpose of this study is to introduce integrated Landside-River Combined System by reducing the risk of floods in urban areas using radar and satellite information.Therefore, we developed the flood forecasting system so people can have enough time to evacuate in case of emergency.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".