Selection algorithm based on Kullback–Leibler distance is one of the simplest, fastest, and most effective methods suitable for feature selection of real applications like leak detection systems. However, this method has problems when the training dataset is not large enough. This paper proposes a crossing level value that evaluates the level of overlap between the conditional probability space and the degree of dispersion of each probability to choose the best features before classifying. The evaluation results indicate the proposed method is more stable, more reliable, and has a higher accuracy than the Kullback–Leibler method.

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability.

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