## 1 Introduction  
The increase in frequency and magnitude of hydrological hazards, such as flash floods, is due to the last decades of climate change at a global scale [1], as well as to the changes affecting land-use/land-cover and land management. The flash floods, generated by the surface runoff on the slopes, represent one of the most dangerous natural hazards producing the greatest damage to human communities. Consequently, it is essential to study and monitor the areas holding a high potential for surface runoff. In the scientific literature, the topic of runoff potential and flash flooding was approached by different authors in their studies [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. At the same time, numerous studies focused on assessing the connection between land-use changes and different features of hydrological hazards, such as the multiplication in number of floods due to the conversion of natural vegetation cover into farmland, increase in floods volume along with built-up space extension, enhancement of runoff through the loss of forested areas, or decrease in frequency of peak discharge by increasing the afforestation [13; 14]. The variety of research directions is connected to numerous factors contributing to land-use change, such as urban extension, intensification of agriculture, deforestation, afforestation, land abandonment, and the like [15; 16; 17]. The decisive role played by these land-use and land-cover changes on the enhancement of hydrological hazards resides in the derived alteration of quantitative relationships between the water cycle elements, such as interception, infiltration, or evaporation [18; 19]. The vegetation cover is highly involved in this context by influencing the evapotranspiration [20; 21; 22], and subsequently, the soil water balance. The geoinformation technologies, namely geographic information systems (GIS) and remote sensing, represent useful tools for assessing the land-use changes, as well as flash-flood potential [23]. The continuous advancements in the field of remote sensing have enabled researchers to obtain satellite images with short revisit cycles and different spatial resolutions, depending on the sensor, for any part of the world. Specifically, Landsat sensors have a sufficient spatial resolution that is detailed enough to characterize the processes that influence the land-use in the study area. The role of GIS is to further process the remotely sensed data as well as other source data to create the vector or raster inputs (i.e., flash-flood inventory and flash-flood predictors) for the selected modelling approaches. Different methods or models can be used for assessing the flash-flood potential/susceptibility. Multi-criteria-based techniques, such as the analytical hierarchy process [24; 25], analytical network process [26]; weighted linear combination [27]; and Viekriterijumsko kompromisno rangiranje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), or Simple Additive Weighting (SAW) techniques [28; 29], represent a group of methods that have been used in a number of flood susceptibility studies. However, the disadvantage of these methods is that they usually rely on subjective evaluations when weighting factors. For that reason, many researchers preferred to use more objective methods that are based on statistics, including frequency ratios, logistic regression, or other bivariate or multivariate statistical models [30; 31; 32; 33]. In recent years, however, more sophisticated data-driven methods have been used for flash-flood susceptibility assessment because they are more robust and have a better capability to handle complicated relationships between input variables. In particular, different machine learning algorithms [34] have been used, including artificial neural networks (ANN) [35], adaptive neuro-fuzzy inference systems (ANFIS) [36], decision trees (DT) [37], or support vector machines (SVM) [38].