###### Abstract  
To overcome the challenges brought about by abnormal weather and the growing industrial water consumption in Taiwan, the Taiwanese government is transporting water from the northern to the southern part of the country to help with droughs occurring in Taoyuan and Hsinchu. In addition, the government invested NTD 2.78 billion to build the backup water pipelines necessary in Taiyuan and Hsinchu, which help ensure a stable and safe water supply required for regional economic development. The construction adheres to the four major strategic goals of \"open source, throttling, dispatch, and backup\". However, the leakage rate of water pipelines remains high. 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. This research established an intelligent sound-assisted water leak identification system, developed and used a localized AI water leak diagnostic instrument to capture on-site dynamic audio, and integrated Internet of Things (IoT) technology to simultaneously identify and locate the leakage. Actual excavation verification results show that the accuracy of the convolutional neural network (CNN) after training is greater than 95%, and the average absolute error calculated between the output data and the input data of the encoder is 0.1021, confirming that the system has high reliability and can reduce the cost of excavation by 26%. deep learning; water leak detection; leak detection; convolutional neural network +
Footnote †: journal: Medical Imaging  
water from the northern to the southern part of the country to help with droughs occurring in Taoyuan and Hsinchu. In addition, they have invested NTD 2.78 billion to build the necessary backup water pipelines, demonstrating the importance of water resources management. The current domestic annual water leakage rate is 14.25%. Suppose the leakage rate of water pipes inside and outside factories can be effectively reduced. In that case, it is estimated that domestic water resources can be strengthened and saved, and domestic economic growth can be stabilized. Most leaking water pipes are found in the urban underground pipe network. The main pipe is used to transport water, and smaller pipes are used to distribute water to users and factories. Hence, the distribution of the pipes is dendritic. At present, it is possible to locate and repair leaking pipes via a program to reduce water loss. A popular technique developed by [PERSON] and Riehle [PERSON] and [PERSON] (2018) over the past 20 years can be used to locate leaks through acoustic/vibration signal analysis. At two access points on either side of the suspected leak location, accelerometers or hydrophones are used to collect vibrational or acoustic signals. Related technology is used to estimate the time delay between two acquisition signals, and the method is commonly used to determine the leakage location in buried pipelines ([PERSON], 2018; [PERSON], 2018; [PERSON], 2018). For the technique to be effective, the propagation velocity of the acoustic signal and the exact pipe length must be known in advance. The study of sound waves propagating in metal or plastic pipes has received much attention for many years. The dispersion propagation model of buried underground water pipelines was studied ([PERSON], 2018; [PERSON], 2018), and the results were explained physically. [PERSON] et al. [PERSON] et al. (2018) developed a low-frequency theoretical model of buried liquid-filled pipelines for predicting wave velocity and validated them experimentally in the case of evacuated pipelines and pipelines buried in soil or water ([PERSON], 2018; [PERSON], 2018). The pipe conditions--such as pipe thickness, material properties, and surrounding media--are known from these studies, and the propagation velocity can be calculated using the corresponding theoretical model.