Enhancing Pipeline Leakage Detection Through Multi-Algorithm Fusion with Machine Learning

Abstract

This paper proposes a pipeline leakage detection technology that integrates machine learning algorithms with Dempster–Shafer (DS) evidence theory. By implementing five machine learning algorithms, this study constructs pipeline pressure and flow signal characteristics through wavelet decomposition. The data were normalized and processed using principal component analysis to prepare the algorithm for training. A new method for constructing basic probability functions using a confusion matrix and a simple support function is proposed and compared with the traditional triangular fuzzy number method. The basic probability function of the identification sample is refined by calculating a comprehensive discount factor. Finally, the results from multiple algorithms are fused using DS evidence theory. Experimental results demonstrate that after combining multiple algorithms, the average accuracy improves by 0.1565%, and the precision of the triangular fuzzy number method is enhanced by 0.091%.