Flood Hazard: A QGIS Plugin for Assessing Flood Consequences
Bibliographic record
Abstract
Flash floods cause substantial harm to the social and economic aspects of the affected countries. This is a significant problem in urban areas where drainage systems are inadequate and unable to withstand severe flooding. Understanding the specific regions that are susceptible to flooding is essential to implement strategies aimed at mitigating the risk. Detecting floods in ungauged basins is challenging. The current work aims to establish a practical method for identifying and mapping floodplain areas. We can use several tools, including the FLO-2D integration tool, Flood Risk tool, Geomorphic Flood Area plugin, and Quantum Geographical Information System (QGIS) with Hydrologic Engineering Centre River Analysis System (HEC-RAS) to efficiently and cost-effectively detect flood hazard zones. The QGIS tool, the Geomorphic Flood Index (GFI), is the most valuable tool for identifying flood-prone areas in cases where the areas are extensive and lack sufficient data. This tool offers high data analysis and cost calculation precision while using few resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".