Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography

Abstract

The article presents four selected methods of supervised machine learning, which can be successfully used in the tomography of flood embankments, walls, tanks, reactors and pipes. A comparison of the following methods was made: Artificial Neural Networks (ANN), Supported Vector Machine (SVM), K-Nearest Neighbour (KNN) and Multivariate Adaptive Regression Splines (MAR Splines). All analysed methods concerned regression problems. Thanks to performed analysis the differences expressed quantitatively were visualized with the use of indicators such as regression, error of mean square deviation, etc. Moreover, an innovative method of denoising tomographic output images with the use of convolutional auto-encoders was presented. Thanks to the use of a convolutional structure composed of two auto-encoders, a significant improvement in the quality of the output image from the ECT tomography was achieved.