Abstract
Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.
Highlights
Water supply pipelines are important infrastructure in cities, and maintaining the stable operation of water supply pipelines has significant economic, sanitary, and environmental worth
To further compare the performance of the two algorithms, we used MLT-KSVC and LST-KSVC to conduct multiple nonlinear classification experiments to obtain optimal hyperparameter C, penalty factor G, and cross-validation accuracy based on these sample points
This paper used the maximum entropy (MaxEnt) model to establish two weight matrices that can be used for classification
Summary
Water supply pipelines are important infrastructure in cities, and maintaining the stable operation of water supply pipelines has significant economic, sanitary, and environmental worth. The real-time monitoring of pipeline operation status and detection of suspected leak risks are significant for maintaining the safe operation of pipe network, avoiding water resource waste, and realizing sustainable production [1]. As a vital technology in the machine learning field, the support vector machine (SVM) [2] and its improved versions are widely utilized in pipeline leak detection and localization. To achieve greater efficiency in leak detection in water pipes, a novel improved multi-class SVM algorithm is proposed, called maximum entropy [3] (MaxEnt) version of LST-KSVC [4] (MLT-KSVC).
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