Flood Susceptibility and Risk Mapping of Kathmandu Valley Watershed, Nepal
Bibliographic record
Abstract
Comprehensive flood risk assessment is often constrained by a lack of appropriate data in high-altitude watersheds, particularly in developing countries like Nepal, where institutional capacities are limited for mapping and monitoring flood-prone communities. This study, one of the first of its kind, produced spatial multi-criteria-based flood susceptibility, vulnerability, and risk index maps for the Kathmandu Valley (KV) watershed in Nepal using an Analytical Hierarchy Process (AHP) approach and Geographical Information System (GIS). The result shows that most parts of the KV (around 80%) have moderate to low flood susceptibility around the watershed but susceptibility is prominent in southern areas. Highly flood-susceptible regions (about 14%) are found mainly along the riverbanks. Flood vulnerability, primarily influenced by population density and literacy rate, is moderate to low in most areas of the watershed (around 86%), predominantly higher in the central urban areas, and gradually lower towards the edge of the watershed. Flood risks in the major portion of the watershed (around 72%), mainly in the southern and eastern parts, are estimated as moderate to low risk, whereas higher risk zones are found in the central urban areas. The high contrast in flood vulnerability scores across the watershed has mainly contributed to the variation of flood risk zones, as flood susceptibility scores are fairly distributed over the watershed. The study findings will help policymakers develop location-specific sustainable flood risk management strategies for the flood-vulnerable communities in the KV watershed.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".