## 1 Introduction
### UAV (Unmanned Aerial Vehicle)  
There are numerous situations in the specialized literature in which studies and simulations of inundation were conducted starting from images taken using drone technology. There are different approaches to this research area. The first approach aims to highlight a methodology for updating data concerning a hydrodynamic model, based on data acquired with the help of drones (UAVs). The model thus proposed is also very effective in the case of the need for the online updating of risk maps in the case of floods and their timely correction [6]. Other approaches in specialized works use artificial intelligence models to develop a model for the automatic identification of flooded buildings using UAV aerial images [7]. There are also very useful studies that used novel ways of measuring water depth for small bodies of water, which are typically more difficult to measure using classical technology [8]. More and more fields identify the opportunity to use drones to collect necessary data in a faster, more precise, and non-intrusive way [9; 10; 11]. Overflowing rivers can be the cause of catastrophic floods, mainly due to their morphological characteristics. These floods can occur due to flow variations, determined by heavy rains and the lack of systematization and regulation of the riverbanks. It is therefore necessary to use combined topo-geodesic and photogrammetric data acquisition techniques to obtain images and geospatial data necessary for the automatic extraction of interesting characteristic attributes [12]. There are different methods we can use to generate DEMs. It has been demonstrated that the use of airborne LiDAR (Light Detecting and Ranging) sensors can produce very accurate and high-resolution DEMs but with higher acquisition and processing costs than in the case of drones equipped with photogrammetric sensors [13; 14]. A comparative approach of UAV-UAS imagery and historical satellite datasets has proven extremely useful, as satellite imagery, such as the Landsat or Sentinel archives, extends decades back in time, allowing for the evaluation of changes over a historical time interval [15; 16; 17; 18]. Several studies have presented and discussed the opportunity of UAV LiDAR data acquisition, referring primarily to the advantage of LiDAR to penetrate vegetation, which leads to an easier classification of points on the ground, of characteristic geometries of interest in the case flood studies, or even the lack of such a classification [19]. The recent development of tools for the study of flood behavior makes it possible to model and simulate them, as well as highlight the characteristics of the drainage network, with the help of the GIS platform, where various data sources can be introduced, analyzed, modeled, and simulated, including those made with the help of UAVs [20; 21; 22; 23; 24]. The combination of UAVs with water-level sensors has proven useful because this approach can provide a unitary and quasi-complete vision of the history and chronology of a flood phenomenon [25]. A careful analysis of a significant sample (about 60) of papers helped to classify the sensors needed for similar studies, highlighting terrestrial photogrammetry at 31.7% and aerial photogrammetry at 26.7%, as the most frequently used low-cost detection method [26]. If we refer to other types of sensors that are useful for estimating and preventing the effects of floods, ground-penetrating radar can obtain, in certain conditions, accurate estimates about the soil and water [27; 28; 29]. It thus becomes obvious that technological advances in unmanned aerial vehicles (UAVs) offer a wealth of capabilities for the efficient and orderly production of images and topographical data, which are useful in the study, modeling, simulation, and prevention of floods [30; 31; 32; 33; 34; 35; 36].