# Increasing the Reliability of Flood Embarments with Neural Imaging Method
## 1 Introduction  
Floods are one of the most frequent natural disasters that have disastrous consequences [1; 2]. One way to enclose the range of flooded areas, in the vicinity of reservoirs and rivers, is to raise the flood embankment level [3]. By doing this, it is possible to reach a higher freshet level over the main river-bed, and therefore to constrain the flood. However, this produces rapidly increasing amounts of water in the valley between the flood embankment, leading to accelerated water flow and flood wave propagation. It causes erosion of the upstream face of the embankment (and of the reservoir) and may destroy it. Additionally, considering the limited filtering capability of the embankment body, high water levels can cause the partial destruction of the embankment. For these reasons, the design of flood embankments with respect to their location, shape and material is extremely complicated. Despite a significant progress in the field of design and construction of flood embankments, there unexpected events may occur. Therefore, the design of a cost-effective, and foolproof, device for the monitoring of the state of flood embankments or reservoirs is critical to prevent the destructive effects of flood events. Methods to prevent natural disasters, such as floods and flooding, through constant monitoring and supervision of technical facilities such as flood embankments, dams and landfills, can be dividedinto two main groups. The first group are traditional methods, whose characteristic feature is the spot measurements. Spot measurements provide information about a small fragment of the flood dam, which makes it uncertain when assessing such threats as water seeping through the internal part of the embankment. The group of traditional methods includes: Geodetic monitoring (detection of structural deformation by manual measurements with benchmarks and automatic using micromirrors), hydrogeological monitoring (detection of anomalies caused by soaking dams by observing piezometric pressures in piezometers installed in the dams and near and distant foreground), geotechnical monitoring (detection of anomalies in the geological structure of the native embankment and reservoir by deep drilling and push probes), seismic monitoring (detection of building stability disturbances by means of accelerometers, which are excited whenever they identify vibrations of a certain level), and visual assessment of technical condition (direct observation conducted by staff) [4]. The second group are methods enabling full visualization of cross-sections of specific flood bank. These are usually methods that use information technology. These methods include specialized IT systems for the analysis of large data sets (big data) generated by systems of many separate sensors located in various places of flood barriers. These methods include Electrical Impedance Tomography (EIT), too. Obtaining a cross-sectional image of a flood embankment with the use of spot sensor systems is very expensive. A large number of sensors requires constant maintenance, which means that the cost of maintaining such monitoring systems for flood dams is very high. In this aspect, tomographic systems are more economical solution. Until now, the main barrier to their widespread use in flood control monitoring was the insufficient resolution of the reconstructed images of the interior of the dams. The neural imaging method presented in this paper, thanks to the use of an original algorithm that uses many neural networks, enables obtaining a high resolution tomographic image. Thanks to this, the method eliminates two main barriers accompanying the problem of monitoring flood embankments--it enables obtaining a holistic image of the cross-section of the dam and generates a high resolution image. Currently, many methods are used to model and monitor flood control dams. In most cases, the practical applications of these methods are made possible by the use of information technology and computers.