Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
Research output: Contribution to journal › Article › Research › peer-review
Authors
External Organisational units
- Skopje Campus
- Faculty of Mechanical Engineering
- Department of Production Engineering
- University of Belgrade
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.
Details
Original language | English |
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Article number | 20220684 |
Number of pages | 17 |
Journal | Open Geosciences |
Volume | 16.2024 |
Issue number | 1 |
DOIs | |
Publication status | Published - 30 Aug 2024 |
Externally published | Yes |