Enhanced Water Leakage Detection and Automation of the Ground Microphone Technology

Water leak detection is a critical component in the protection and efficient management of distribution networks. Undetected leaks can result in significant structural damage, costly repairs, business interruptions, and loss of valuable assets. The system must achieve high sensitivity to detect faint leak sounds in diverse pipe materials (metal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$/$</tex> non-metallic), incorporate noise suppression to isolate leaks from ambient interference (e.g., traffic or machinery), enable automation for real-time detection and classification without expert intervention, and ensure scalability to adapt to urban and rural water distribution networks. The system faces background noise contamination from fluid dynamics, environmental sources, and pipe vibrations; limited accuracy in non-metallic pipes due to rapid acoustic signal attenuation; manual dependency in traditional methods, resulting in delays and higher costs; and false alarms caused by overlapping frequencies between leaks and ambient noise. Early leak identification prevents catastrophic pipe bursts, reduces repair costs by up to 70 %, and supports sustainability goals by conserving water resources. This paper addresses critical challenges in water leak detection by integrating ground microphone technology with advanced signal processing and machine learning, offering a novel solution for automated leak classification, and using an ultra-low noise amplifier for noise reduction. It consists of four stages such as signal acquisition, signal division, signal amplification, and automation. By employing an ultra-low noise amplifier and filtering, the system achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 0 - 6 0 ~ d B}$</tex> noise suppression, effectively isolating leaks from ambient interference such as traffic or pipe vibrations. This enhancement enables reliable detection even in non-metallic pipes, where traditional methods struggle due to rapid acoustic signal attenuation. For feature extraction, Support Vector Machines (SVM) are utilized to identify discriminative spectral and temporal patterns, enhancing the separability of leak signatures from noise. It achieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 2 \%}$</tex> precision in leak classification, outperforming thresholdbased systems by accurately distinguishing between small and large leaks.