Increasing the Reliability of Flood Embankments with Neural Imaging Method.

Featured Application: The proposed neural imaging method helps improve the functionality of widely used tomographic methods. The presented method is suitable to monitor the protections of the tailings ponds and flood embankments. This paper presents an innovative system of many artificial neural networks that enables the tomographic reconstruction of the internal structure of a flood embankment. An advantage of the proposed method is that it allows us to obtain high-resolution images, which essentially contributes to early, precise and reliable prediction of operational hazards. The method consists in training a cluster of separate neural networks, each of which generates a single point of the output image. The simultaneous and parallel application of the set of neural networks led to effective reconstruction of the internal structure of a deposition site for floatation tailings. Results obtained from the study allow us to solve the low resolution problem that usually occurs with non-invasive imaging methods. This effect was possible thanks to the design of a new intelligent image reconstruction system. [ABSTRACT FROM AUTHOR]