Identification and mapping of multi-type flood hotspots using an ensemble technique in the transboundary of Kabul River Basin
Bibliographic record
Abstract
The Kabul River Basin (KRB), located in the eastern Hindukush region, is a transboundary basin shared by Afghanistan and Pakistan. This study aims to identify and map hotspots for five major flood types: ephemeral, fluvial, flash, urban, and glacial lake outburst floods (GLOFs) using three models: the Frequency Ratio (FR), Expert Opinion-based Analytical Hierarchy Process (EO-AHP), and Prediction Rate-based Analytical Hierarchy Process (PR-AHP). By analyzing 620 flood locations and 18 flood predictors, we found that 14 %, 20 %, and 15 % of the study area derived from FR, EO-AHP, and PR-AHP models were highly susceptible to multi-type floods, respectively, highlighting susceptible hotspots of different floods in the KRB. This study provides a comprehensive multi-type flood susceptibility map for the KRB. The findings of this study are crucial for effective flood management, as they identify and integrate multi-type floods into a single map. The study helps relevant authorities in prioritizing resource allocation, improving early warning systems, and implementing sustainable land use planning, ultimately improving flood preparedness and building resilience through risk-informed policy-making. • Identify and map multi-type flood hotspots in the Kabul River Basin using three models. • Assess susceptibility to ephemeral, fluvial, flash, urban, and glacial lake outburst floods. • 14 %, 20 %, and 15 % of the study area is susceptible to multi-type floods. • An innovative ensemble prediction rate-based analytical hierarchy process model has been introduced.
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 | 0.000 |
| 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".