Using Convolutional Neural Networks in the Development of a Water Pipe Leakage and Location Identification System

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 droughts 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%.