Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
Bibliographic record
Abstract
Abstract This study presents a comprehensive analysis of flash flood susceptibility in the Kratovska Reka catchment area of Northeastern North Macedonia, integrating Geographic Information System, remote sensing, and field survey data. Key factors influencing flash flood dynamics, including Slope, Lithology, Land use, and Vegetation index, were investigated to develop the Flash Flood Potential Index (FFPI). Mapping slope variation using a 5-m Digital Elevation Model (DEM) revealed higher slopes in eastern tributaries compared to western counterparts. Lithological units were classified based on susceptibility to erosion processes, with clastic sediments identified as most prone to flash floods. Land use analysis highlighted non-irrigated agricultural surfaces and areas with sparse vegetation as highly susceptible. Integration of these factors into the FFPI model provided insights into flash flood susceptibility, with results indicating a medium risk across the catchment. The average value of the FFPI is 1.9, considering that the values range from 1 to 5. Also, terrains susceptible to flash floods were found to be 49.34%, classified as medium risk. Field survey data validated the model, revealing a significant overlap between hotspot areas for flash floods and high-risk regions identified by the FFPI. An average FFPI coefficient was calculated for each tributary (sub-catchment) of the Kratovska Reka. According to the model, Latišnica had the highest average coefficient of susceptibility to potential flash floods, with a value of 2.16. These findings offer valuable insights for spatial planning and flood risk management, with implications for both local and national-scale applications. Future research directions include incorporating machine learning techniques to enhance modeling accuracy and reduce subjectivity in assigning weighting factors.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".