Montenegro has different influences on the weather and climate; in general, according to Köppen’s classification, there are two climate zones: warm temperate (C) and cold temperate (D). The aim of this study is to determine the susceptibility to wildfires in the Montenegrin coastal municipality of Budva and the northern municipality of Rožaje, which are located in different climatic conditions, using multicriteria GIS decision analysis (GIS-MCDA). Nine natural and anthropogenic criteria were used for the analysis. Open geospatial data were used as input data for all criteria. The assignment of weighting coefficients for the criteria in relation to wildfire susceptibility importance was based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (F-AHP) procedures. The results for the AHP and F-AHP models were obtained using the Weighted Linear Combination (WLC) method. According to the AHP model, the very high and high category covers 80.93% of the total area in Budva and 80.65% in Rožaje. According to the F-AHP model, the very high and high category occupies 80.71% of the total area in Budva and 82.30% in Rožaje. The validation shows that the models of GIS-MCDA perform fair in both climatic zones. The proposed models, especially in the absence of geospatial data, can be a strategic and operational advantage in the development of plans and strategies for protection against wildfires.

Soil erosion and wildfires are frequent natural disasters that threaten the environment. Identifying and zoning susceptible areas are crucial for the implementation of preventive measures. The Šar Mountains are a national park with rich biodiversity and various climate zones. Therefore, in addition to protecting the local population from natural disasters, special attention must be given to preserving plant and animal species and their habitats. The first step in this study involved collecting and organizing the data. The second step applied geographic information systems (GIS) and remote sensing (RS) to evaluate the intensity of erosion using the erosion potential model (EPM) and the wildfire susceptibility index (WSI). The EPM involved the analysis of four thematic maps, and a new index for wildfires was developed, incorporating nine natural and anthropogenic factors. This study introduces a novel approach by integrating the newly developed WSI with the EPM, offering a comprehensive framework for assessing dual natural hazards in a single region using advanced geospatial tools. The third step involved obtaining synthetic maps and comparing the final results with satellite images and field research. For the Šar Mountains (Serbia), high and very high susceptibility to wildfires was identified in 21.3% of the total area. Regarding soil erosion intensity, about 8.2% of the area is affected by intensive erosion, while excessive erosion is present in 2.2% of the study area. The synthetic hazard maps provide valuable insights into the dynamics of the erosive process and areas susceptible to wildfires. The final results can be useful for decision-makers, spatial planners, and emergency management services in implementing anti-erosion measures and improving forest management in the study area.

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km² of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km² would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures.

Assessing flash flood susceptibility is crucial for disaster management, yet Montenegro lacks research using geoinformation technologies. In northeastern Montenegro, the Ibar River Basin, mainly in Rožaje, has a well-developed hydrological network with torrential streams prone to flash flooding. This study compares two multi-criteria GIS decision analysis (GIS–MCDA) methodologies, the Analytic Hierarchy Process (AHP) and the Best-Worst Method (BWM), for assessing flood susceptibility. The analysis uses the Flash Flood Susceptibility Index (FFSI), integrating geoenvironmental and climatic factors. The geoenvironmental criteria considered include terrain slope, distance from the drainage network, geology, land cover, drainage density, bare soil index, and the BIO16 variable, which represents the mean monthly precipitation of the wettest quarter to enhance precipitation pattern assessment. The AHP model classifies 2.78% of the area as high to very high susceptibility, while the BWM model identifies 3.21% in these categories. Both models perform excellently based on AUC values, with minor, non-significant differences. Sensitivity analysis shows AHP provides a more stable weight distribution, whereas BWM is more sensitive to weight changes, emphasizing dominant criteria more strongly. This study introduces BWM for the first time in flash flood modeling, demonstrating its suitability for susceptibility assessment. The key novelty lies in its comparative analysis with AHP, highlighting differences in weight distribution and model stability.

This work aims to determine the current state of sediment production and propose land use measures that will affect the reducing the intensity of soil erosion for the areas of the Polimlje drainage basin on the territories of Montenegro and Serbia, and the small Shirindareh sub-basin of Iran. The approach is based on field and laboratory methods, which are processed by Web-based Intensity of Erosion and Outflow (WIntErO)model used to calculate erosion intensity. By using the computer-graphical method of the "WIntErO" software, in the study of erosion intensity, surface values (watershed surface, surface between isohypsies, etc.) and length, i.e. deviations from the map (length of the main watercourse, length of the watershed line, etc.) is processed very precisely, which was not the case before when using mechanical instruments, planimeters and curvimeters. The new WIntErO model is an integrated computer-graphic program package of the third-generation method based on the earlier generations of the modelling tools "River basins", and IntErO and calculates the amount of sediment, the intensity of soil erosion, as well as the maximum runoff from the basin, according to the EPM model of Gavrilović.During the procedure, an accuracy assessment is conducted with measurements of reservoir sediment deposition. These measurements were performed in April 2017 using professional hydrographic recording equipment, following the same methodology as in 2012. The measurement of point locations was conducted using a GPS receiver and a Trimble R6 base station. The reservoir's depth was measured using a single-frequency portable echo sounder, specifically the Odom Hydro Track. The initial assessment shows fairly acceptable results of the implemented WIntErO modelling for both study areas. Z coefficient values ranging from 0.01 to 1.00 for the observed period indicate that the river basins delineate areas with varying levels of susceptibility to water erosion processes—ranging from very low to moderate and high risk—within the studied drainage basins. Based on the analysis, we found that the average erosion intensity in Polimlje is 331.78 m³ km⁻² year⁻¹ per square kilometer. For the Shirindareh sub-basins, the average actual soil losses per square kilometer are 201 m³ year⁻¹ km⁻². Given these findings, it is evident that these basins require prompt implementation of soil conservation measures.