# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 1 Introduction  
Water is an indispensable and important substance for the survival of human beings and all living things and is an irreplaceable and extremely valuable natural resource for industrial and agricultural production, economic development, and environmental improvement. However, many countries in the world are facing the problem of water shortage. Take China as an example. Although China is a water-rich nation, it also suffers from severe water shortages on a per capita basis. According to statistics, China's per capita water resources comprise less than 2200 cubic meters, less than one-third of the world's per capita water resources [1]. Among the 660 cities in China, approximately 400 cities are short of water. With the development and construction of the country, the water usage rates of various cities are increasing year by year, and the lengths of urban water supply pipelines are also increasing. Consequently, society places higher demands on the quality of urban water supply networks, which is crucial for ensuring stable development [2, 3, 4]. However, in terms of the construction of China's overall municipal water supply network, many places still have the problem of pipeline leakage, which causes residents to be forced to cut off water and even causes accidents such as road collapse, resulting in the wastage of water resources and economic damage. The primary problem for reducing the leakage ratein a city concerns how to accurately know that the water supply pipeline has leaked, and it is necessary to accurately locate the location of the leakage point so as to be able to quickly repair and reduce the wastage of resources. Therefore, it is necessary to develop a system that can monitor the precise location of leaks in real time when leaks occur in the water supply network. Mainstream pipeline leak detection technologies include the magnetic flux leakage (MFL) detection method, pipeline robot inspection method, water pressure prediction method, and Distributed Acoustic Sensing (DAS) method. The MFL detection method is a commonly used method for pipeline inspection [5]. The principle behind MFL detection involves fully magnetizing the pipeline, causing magnetic field lines to deform at the locations of defects. These deformed lines flow out from a defect and then return to the interior of the pipeline. Sensors carried by the inspection system detect and store this information, allowing the accurate localization of pipeline leaks. While this approach offers high precision in leak localization, it is limited to ferromagnetic pipelines. The pipeline robot inspection method uses robots within the pipelines to monitor their condition. Simultaneously, a wireless sensor network is installed on the ground to receive data [6,7]. However, this approach faces several challenges. Firstly, in practical scenarios, pipes are often buried deep underground, hindering wireless signal transmission. Additionally, there is a risk of robots becoming stuck within the pipes, leading to blockages. The water pressure prediction method uses water pressure values to predict water leakage. [PERSON] et al. employed Support Vector Machines (SVMs) to classify data from the EPANET hydraulic modeling system, resulting in improved leak prediction [8]. [PERSON] et al. further enhanced leak prediction accuracy through Bayesian learning processes [9]. The proposed solution suffers from a limitation: it cannot provide precise information about the exact locations of leaks. Distributed Acoustic Sensing (DAS) tackles water leakage detection by capturing vibration signals associated with leak locations. DAS is widely applicable to both ferromagnetic and non-ferromagnetic pipelines, offering the precise localization of vibration signals. In 2015, [PERSON] et al. successfully detected water pipe leaks by deploying communication optical fibers along the inner walls of pipelines, achieving leak detection when under conditions where the aperture exceeded 4 mm and internal pressure exceeded 0.2 MPa [10]. In 2022, [PERSON] et al.