###### Abstract
In practice, if some pipeline conditions are unknown, the propagation velocity can be measured in situ using known simulated leaks, for example, by releasing water from a fire hydrant ([PERSON], 2018). Furthermore, although the Taiwan Water Corporation constructs a community pipe network for the water pipeline system in each region and determines whether there are water leaks through the monitoring of changes in pressure and flow, the cost of this method is high owing to the need to install water meters (pressure gauges and flow meters). At present, the pipelines in urban areas are generally PVC pipelines, and the pipeline pressure is below 2 kg/cm\({}^{2}\). The leakage signal rapidly decays, and it is difficult to effectively monitor long-distance leakages, unlike in a steel pipe system. The community pipeline network can only provide information on whether the area contains a leak and cannot provide the exact location of the leak for excavation and repair. Therefore, in practice, it remains necessary for experienced leak detectors to conduct on-site leak detection in the area to determine the location of the leak. However, when conducting sound-based leak detection on-site, interference from ambient sound often causes the leak detector to misjudge, resulting in errors in leak detection and lowering leak repair efficiency. Take the international manufacturers of leak detection instruments (SEWERIN and EchoShore) as an example: their leak detection equipment performs leak detection via frequency band variation and manual judgment, which is easily affected by environmental noise. In addition, pipes suitable for leak detection equipment are made from rigid materials, differing from the current situation in Taiwan, where most main water pipelines are still PVC pipelines. This makes it difficult for the equipment to be adapted for use in the localized water pipeline transportation system. The above helps us understand the importance of leak detection. The traditional judgment of water pipe leakage depends on the judgment of professionals, but its efficiency is very low. To help with large-scale right-of-way applications and the timeliness of emergency repairs, establishing a system that can detect the locations of leakages is vital. This study intended to apply artificial intelligence (AI) deep learning to develop a water pipe leakage and location identification system. In recent years, artificial intelligence (AI) applications have continued to thrive in various industries, and deep learning has been widely used. Since deep learning can be used to perform deep analysis of signals and learn the implicit structure of signals, trained models are less affected by environmental noise, unmatched recording settings, and other factors. Therefore, these models are more suitable to be applied to sound event detection systems with strong key properties [11]. Deep learning has proved to be a very powerful tool because of its ability to handle large amounts of data [12; 13]. Among them, the convolutional neural network (CNN) is widely used. The structure of the convolutional neural network enables it to use the two-dimensional structure of input data; compared with other deep learning architectures, CNNs can produce better results in image and speech recognition. This research established an intelligent sound-assisted water leak identification system by developing and using a localized AI water leak diagnostic instrument to capture on-site dynamic audio, applying a CNN for leak detection, and integrating Internet of Things (IoT) technology to simultaneously identify and locate leakages. In addition, the system was imported into a personal handheld device or a back-end platform. The leak detector can interact with the site remotely to assess the current status of the pipelines, which greatly enhances the efficiency of water leakage detection and provides a wider range of pipeline status information. The system will be beneficial to big data analysis on cloud platforms in the future and help predict and prevent pipeline leakages.