###### Abstract  
Conventional schemes to detect leakage in water pipes require leakage exploration experts. However, to save time and cost, demand for sensor-based leakage detection and automated classification systems is increasing. Therefore, in this study, we propose a convolutional neural network (CNN) model to detect and classify water leakage using vibration data collected by leakage detection sensors installed in water pipes. Experiment results show that the proposed CNN model achieves an F1-score of 94.82% and [PERSON]'s correlation coefficient of 94.47%, whereas the corresponding values for a support vector machine model are 80.99% and 79.86%, respectively. This study demonstrates the superior performance of the CNN-based leakage detection scheme with vibration sensors. This can help one to save detection time and cost incurred by skilled engineers. In addition, it is possible to develop an intelligent leak detection system based on the proposed one. water pipeline; leak detection; FFT; deep learning; CNN +
Footnote †: journal: Medical  
error of the cross spectrum and power spectral density [9]. Similarly, the mel-frequency cepstral coefficients of acoustic signals were used to detect leaks based on pipeline data [10]. Many studies on leak detection through machine learning have been conducted. For example, a leak detection technology based on convolutional neural networks (CNN) using thermal images was proposed to solve the problem of poor leak detection performance due to the lack of experts and reliance on the skills of individual skilled workers [11]. In this study, research was conducted to develop a smart water management system based on deep learning and machine learning by improving the binary classification performance of normal and abnormal (leak) cases. A study was also conducted to apply a multi-strategy ensemble learning approach to sound signals using a gradient boosting tree classification model [12]. Similarly, a deep neural network model is also adopted to perform leak detection [13]. In addition, leak detection in pipelines carrying fluids, gases, and water has been considered in various tasks [14; 15]. Additionally, studies have considered detecting leakage with robots equipped pressure and acceleration sensors [16; 17; 18]. Recently, approaches based on artificial intelligence models have shown considerable success in similar areas, such as oil and gas pipelines, other than water pipelines. A 2D CNN model and a long short-term memory autoencoder (LSTM AE) with accelerometers mounted on a pipeline wall are applied for oil and gas leak detection. It is reported in the study that the supervised learning-based CNN model outperforms the unsupervised LSTM AE model [19]. Furthermore, the combination of both models is also proposed to obtain better performance [20]. Another unique approach is based on a CNN model with a kind of swarm intelligence optimization algorithm, the sparrow search algorithm [21]. In various engineering fields besides pipeline leak detection problems, CNN models are widely adopted in classification problems to solve detection problems that depend on the experience of engineers [22; 23]. It is observed that the CNN models are employed for feature extraction automation even in the prediction applications [24]. In addition, there was a research result of detecting a leak in a transmission main operating in a real-world environment through transient test-based techniques (TTBTs). TTBTs are based on the transient event dynamics of a pressurized flow. A pressure wave generated along a pressurized pipeline interacts with any leak. As a result, in the first phase of the transient, the leak is detected by utilizing the reflected feature to the place where the pressure wave of lower amplitude is generated. For example, leaks generate negative pressure waves, whereas partial blockages generate both positive and negative pressure waves [25; 26].