Divisional intuitionistic fuzzy least squares twin SVM for pipeline leakage detection

Abstract

Improving the accuracy of pipeline leak detection in noisy environments is significant for maintaining pipeline safety. Currently, the weighted support vector machine (SVM) is extensively employed. Among them, Intuitionistic fuzzy twin SVM (IFTSVM) has good robustness due to the integration of intuitionistic fuzzy set theory. However, IFTSVM does not fully consider the position information of samples in the feature space, cannot reduce the impact of heterogeneous clustering noise. Moreover, it requires solving two quadratic programming problems (QPPs), which is inefficient. To address the aforementioned issues, this paper proposes a divisional intuitionistic fuzzy least squares twin SVM (DIFL-TSVM) for pipeline leakage detection. This approach proposes a novel divisional intuitionistic fuzzy membership calculation strategy to reduce the impact of heterogeneous clustering noise and simplify the calculation of sample membership. In addition, DIFL-TSVM transforms the original QPPs into two linear equations using the least squares method to improve the efficiency of model. This paper verifies that the DIFL-TSVM model not only has the ability to eliminate the effects of discrete noise and heterogeneous clustering noise but also more efficiency on water supply pipeline leakage diagnosis experiments and UCI datasets. The experimental results show that DIFL-TSVM has higher leak identification with a detection accuracy of 95.6 %.