Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
Bibliographic record
Abstract
Abstract The subject of this research is the analysis of flood dynamics in the Ukrina River Basin, Bosnia and Herzegovina, using a remote sensing-based approach and geographic information systems during the period 2016–2019. The aim is to identify the spatial and temporal patterns of floods by integrating satellite-derived precipitation data, hydrological data, and Sentinel-1 imagery processed through Google Earth Engine. The methodology included the use of CHIRPS precipitation data and the Standardized Precipitation Index (SPI) for identifying meteorological anomalies, while Sentinel-1 SAR data were used to map flood extent based on radar backscatter change detection. The approach combined temporal analysis with spatial overlays of land use and administrative boundaries to assess affected areas. Flood events were identified in January 2016 (89.98 ha), March 2017 (179.85 ha), March 2018 (58.81 ha), and May 2019 (195.38 ha), coinciding with periods of above-average precipitation (>125%), positive SPI values, and elevated water levels. The spatial analysis of flooded areas, overlaid with land use data, revealed that agricultural land was the most affected category, with 79.21 ha flooded in 2016, 169.15 ha in 2017, 48.89 ha in 2018, and 184.90 ha in 2019. Built-up areas were also significantly impacted, posing risks to infrastructure and economic stability. The cities and municipalities of Derventa, Prnjavor, and Stanari were most frequently affected by floods during the study period. The findings highlight the role of cumulative precipitation and hydrological conditions in triggering flood events and provide insights for flood risk management, including adaptive strategies, early warning, and sustainable land use planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".